Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average

Energy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.Comment: 7 pages, 6 figure

Energy-Efficient Node Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. This heterogeneous node deployment problem is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where nodes retain their deployed position, and mobile WSNs where nodes can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal node deployment in static WSNs, we propose an iterative algorithm to deploy nodes. In addition, we study the necessary conditions of the optimal movement-efficient node deployment in mobile WSNs with constrained movement energy, and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed node deployment algorithms outperform the existing methods in the literature, and achieves a lower total wireless communication power in both static and mobile WSNs, on average

Node Deployment in Heterogeneous Rayleigh Fading Sensor Networks

We study a heterogeneous Rayleigh fading wireless sensor network (WSN) in which densely deployed sensor nodes monitor an environment and transmit their sensed information to base stations (BSs) using access points (APs) as relays to facilitate the data transfer. We consider both large-scale and small-scale propagation effects in our system model and formulate the node deployment problem as an optimization problem aimed at minimizing the wireless communication network's power consumption. By imposing a desired outage probability constraint on all communication channels, we derive the necessary conditions for the optimal deployment that not only minimize the power consumption, but also guarantee all wireless links to have an outage probability below the given threshold. In addition, we study the necessary conditions for an optimal deployment given ergodic capacity constraints. We compare our node deployment algorithms with similar algorithms in the literature and demonstrate their efficacy and superiority

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

Distributed Coding/Decoding Complexity in Video Sensor Networks

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality

Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of N UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment. We also derive in closed-form the asymptotic optimal common flight height of $N$ UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent

5G Cellular: Key Enabling Technologies and Research Challenges

The evolving fifth generation (5G) cellular wireless networks are envisioned to provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. This article presents several emerging technologies, which will enable and define the 5G mobile communications standards. The major research problems, which these new technologies breed, as well as the measurement and test challenges for 5G systems are also highlighted.Comment: IEEE Instrumentation and Measurement Magazine, to appear in the June 2015 issue. arXiv admin note: text overlap with arXiv:1406.6470 by other author