A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research

Strengths and Weaknesses of Prominent Data Dissemination Techniques in Wireless Sensor Networks

Data dissemination is the most significant task in a Wireless Sensor Network (WSN). From the bootstrapping stage to the full functioning stage, a WSN must disseminate data in various patterns like from the sink to node, from node to sink, from node to node, or the like. This is what a WSN is deployed for. Hence, this issue comes with various data routing models and often there are different types of network settings that influence the way of data collection and/or distribution. Considering the importance of this issue, in this paper, we present a survey on various prominent data dissemination techniques in such network. Our classification of the existing works is based on two main parameters: the number of sink (single or multiple) and the nature of its movement (static or mobile). Under these categories, we have analyzed various previous works for their relative strengths and weaknesses. A comparison is also made based on the operational methods of various data dissemination schemes

Effective Node Clustering and Data Dissemination In Large-Scale Wireless Sensor Networks

The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals that have been identified by researchers as necessary in order to further expand the deployment of such networks for their use in various applications. This thesis aims to provide an energy efficient and effective node clustering and data dissemination algorithm in large-scale wireless sensor networks. In the area of clustering, the proposed research prolongs the lifetime of the network by saving energy through the use of node ranking to elect cluster heads, contrary to other existing cluster-based work that selects a random node or the node with the highest energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a level of universal awareness of existing nodes in the subject area and to avoid the problem of disconnected or forgotten nodes. In the area of data dissemination, the aim of this research is to effectively manage the data collection by developing an efficient data collection scheme using a ferry node and applying a selective duty cycle strategy to the sensor nodes. Depending on the application, mobile ferries can be used for collecting data in a WSN, especially those that are large in scale, with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry-based approach thus eliminates, or minimizes, the need for the multi-hop forwarding of data, and as a result, energy consumption at the nodes will be significantly reduced. This is especially true for nodes that are near the base station as they are used by other nodes to forward data to the base station. MATLAB is used to design, simulate and evaluate the proposed work against the work that has already been done by others by using various performance criteria

Relay Node Placement and Trajectory Computation of Mobile Data Collectors in Wireless Sensor Networks

Recent research has shown that introducing mobile data collectors (MDC) can significantly improve the performance of wireless sensor networks. There are important design problems in this area, such as determining the number and positions of relay nodes, determining their buffer capacities to ensure there is no data loss, and calculating a suitable trajectory for MDC(s). In this thesis, we first propose an integrated integer linear program (ILP) formulation that calculates the optimal number and positions of the relay nodes with the requisite buffer capacities. We then present two algorithms for calculating the trajectory of the MDC, based on the locations and the load of each individual relay node, in a way that minimizes the energy dissipation of the relay nodes. Our simulation results demonstrate that our approach is feasible for networks with hundreds of sensor nodes and leads to significant improvements compared to conventional data communication strategies

On the Data Gathering Capacity and Latency in Wireless

In this paper, we investigate the fundamental properties of data gathering in wirelesssensor networks, in terms of both transport capacity and latency. We consider a scenarioin which s(n) out of n total network nodes have to deliver data to a set of d(n) sink nodesat a constant rate f(n; s(n); d(n)). The goal is to characterize the maximum achievablerate, and the latency in data delivery. We present a simple data gathering scheme thatachieves asymptotically optimal data gathering capacity and latency with arbitrary net-work deployments when d(n) = 1, and for most scaling regimes of s(n) and d(n) whend(n) > 1 in case of square grid and random node deployments. Differently from mostprevious work, our results and the presented data gathering scheme do not sacrifice en-ergy efficiency to the need of maximizing capacity and minimizing latency. Finally, weconsider the effects of a simple form of data aggregation on data gathering performance,and show that capacity can be increased of a factor f(n) with respect to the case of nodata aggregation, where f(n) is the node density. To the best of our knowledge, theones presented in this paper are the first results showing that asymptotically optimal datagathering capacity and latency can be achieved in arbitrary networks in an energy efficientway

Interference-aware multipath video streaming in vehicular environments

The multipath transmission is one of the suitable transmission methods for high data rate oriented communication such as video streaming. Each video packets are split into smaller frames for parallel transmission via different paths. One path may interfere with another path due to these parallel transmissions. The multipath oriented interference is due to the route coupling which is one of the major challenges in vehicular traffic environments. The route coupling increases channel contention resulting in video packet collision. In this context, this paper proposes an Interference-aware Multipath Video Streaming (I-MVS) framework focusing on link and node disjoint optimal paths. Specifically, a multipath vehicular network model is derived. The model is utilized to develop interference-aware video streaming method considering angular driving statistics of vehicles. The quality of video streaming links is measured based on packet error rate considering non-circular transmission range oriented shadowing effects. Algorithms are developed as a complete operational I-MVS framework. The comparative performance evaluation attests the benefit of the proposed framework considering various video streaming related metrics