The rapid growth in multimedia services and diverse mobile technologies have recently brought about sudden surges in traffic demands. To keep up with this trend, small cells are considered as a promising solution to s...
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ISBN:
(纸本)9781509059355
The rapid growth in multimedia services and diverse mobile technologies have recently brought about sudden surges in traffic demands. To keep up with this trend, small cells are considered as a promising solution to supplement macrocell coverage and enhance network capacity in next generation cellular networks. However, the isotropic characteristics of conventional omni-directional antennas installed in small cells reveal a trade-off between coverage and power consumption. Boosted power levels allow for larger coverage areas while resulting in serious interference among cells. On the other hand, although weak power emissions can mitigate interference, coverage holes may appear instead. To overcome the physical limitations, a beamforming technique called switched multi-element antenna (SMEA) is introduced in this work, which is capable of shaping kinds of radiation patterns toward desired users. We propose an efficient radiation pattern selection scheme using SMEA, with the objective of maximizing the total number of served users within the coverage ranges of small cells. We then construct a series of simulations to evaluate the performance of our proposed scheme. Compared with a uniform power control scheme using omnidirectional antenna and a distributed pattern selection scheme using SMEA, simulation results show that our proposed scheme can significantly enhance coverage and deal with the trade-off issue as well.
Visual sensors can be used to retrieve still images or video streams in a large set of monitoring applications, providing valuable information of the monitored environment. In fact, camera-enabled sensors are directio...
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Visual sensors can be used to retrieve still images or video streams in a large set of monitoring applications, providing valuable information of the monitored environment. In fact, camera-enabled sensors are directional and the viewed area depends on their orientations. As the positions and orientations of the embedded cameras may not be optimal after deployment, the area viewed by sensors may be adjusted using some optimization algorithm. Although the problem of coverage optimization has been addressed by some research works, coverage maximization arises as a challenging problem where the number of camera views over a set of targets has to be maximized for higher availability and increased number of viewed perspectives of targets. This paper proposes centralized algorithms to compute orientations of rotatable cameras to achieve maximized coverage in wireless visual sensor networks, bringing significant results to this research area.
This paper takes into consideration the problems related to monitoring a phenomenon of interest in an unknown and open environment using multiple mobile sensor (MS) nodes. We propose an environment learning-based phen...
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This paper takes into consideration the problems related to monitoring a phenomenon of interest in an unknown and open environment using multiple mobile sensor (MS) nodes. We propose an environment learning-based phenomenon monitoring system that iteratively learns about the environment and relocates MS nodes to optimal positions, where MS nodes can attain a high weighted sensing coverage and maintain network connectivity. In this paper, finding optimal positions for MS nodes is defined as the connectivity-constrained coverage maximization problem. An integer linear programming optimization formulation is proposed to find the solution. We also propose three heuristics algorithms to efficiently solve the connectivity-constrained coverage maximization problem. Simulation results show that the proposed algorithms outperform other approaches in terms of the weighted coverage efficiency and energy efficiency.
Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low-energy, and fault-tolerant routing pr...
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Mobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low-energy, and fault-tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time-sensitive application scenarios.
Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the s...
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Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants' precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants' location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber's geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.
This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmann...
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This paper presents a molecular force-based deployment algorithm of charging stations according to the principle of intermolecular forces in physics to expand the flight coverage of electric-powered multi-rotor Unmanned Aerial Vehicles (UAV). With the help of this algorithm, a multi-rotor UAV can reach anywhere in the specific area by charging at the charging station several times. In this algorithm, a number of equal circles are used to cover the specific area (in a two-dimensional plane), and the center of each circle denotes a charging station. The radius of these circles is equal to the radius of action of the UAV. The number of the circles is set by the users. Under the combined effect of three virtual forces, the centers of the circles, called nodes, keep moving within the specific area, and multiple iterations are performed to adjust the location of each node. Finally, a proper deployment scheme for the charging stations is generated, which can achieve the working area maximization of the UAV by a certain number of charging stations. Simulation experiments were executed, and the results under different conditions show that the proposed algorithm can meet the expected requirements and has an advantage over three other algorithms in terms of coverage ratio. The experiment results also indicate that in the case of dense node density, the proposed algorithm has a better coverage performance than the case of sparse node density. The experimental data are available at . The codes will be published later.
The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and proces...
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The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for "Maintaining Connectivity and coverage maximization" called MCCM. The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes' movement over existing algorithms in the state of the art.
Two of the main challenges in wireless sensor networks (WSNs) are connectivity and coverage. Connectivity keeps different nodes in the network linked and to exchange data. coverage affects the efficiency of the operat...
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ISBN:
(纸本)9781728150529
Two of the main challenges in wireless sensor networks (WSNs) are connectivity and coverage. Connectivity keeps different nodes in the network linked and to exchange data. coverage affects the efficiency of the operating sensors used in the network. This paper proposes a novel resilient incremental algorithm that improves the coverage of randomly distributed mobile devices within a heterogeneous or homogeneous environment. This algorithm guarantees connectivity by ensuring at least 2-connected neighbors for any device in the network. Results showed up to 89% coverage improvement in a heterogeneous environment and up to 99% coverage improvement in a homogeneous environment.
A method to maximize the total coverage of multiple unmanned aerial vehicles (UAVs) which monitor a bounded space is presented. The goal of all UAVs is to maximize their individual coverage while minimize possible cov...
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ISBN:
(纸本)9798350307573;9798350307566
A method to maximize the total coverage of multiple unmanned aerial vehicles (UAVs) which monitor a bounded space is presented. The goal of all UAVs is to maximize their individual coverage while minimize possible coverage overlaps among them. This goal is achieved using a multi-agent reinforcement learning (MARL) method which is embedded with a coordination strategy that allows several UAVs to negotiate their actions to avoid possible overlaps between their coverage. Simulation results are shown to illustrate the developed MARL scheme's performance.
With the occurrence of a disaster, the conventional cellular network becomes non-functional. To provide connectivity to the affected users in such a scenario, we propose a novel multi-hop device-to-device (D2D) commun...
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With the occurrence of a disaster, the conventional cellular network becomes non-functional. To provide connectivity to the affected users in such a scenario, we propose a novel multi-hop device-to-device (D2D) communication framework to connect to an active base station (BS). The goal of the proposed work is to maximize the number of covered users in the disaster-affected area within a given time frame. Joint routing and scheduling is imperative in a multi-hop network;however, the existing works on joint routing and scheduling optimization consider that the source-destination (user-BS) pairs are known beforehand or fixed. This is an inefficient approach when maximizing the number of covered users in a time-bounded communication set-up. Consequently, we propose a novel multi-hop D2D framework with joint source-destination pairing, routing and scheduling optimization. The optimization problem is formulated as an integer linear programming (ILP) problem. Further, due to the high time complexity of ILP, a low complexity graph-based scheduling constraint aware routing and pairing algorithm is proposed, resulting in a significant reduction in processing time compared to the optimal solution. The proposed algorithm also outperforms shortest path routing based scheduling in terms of users covered in the disaster-affected area.
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