This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Network Simulator NS2

There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains. From an industrial perspective, WNs present many advantages including flexibility, low cost, easy deployment and so on. Therefore, WNs can play a vital role in the Industry 4.0 framework, and can be used for smart factories and intelligent manufacturing systems. In this paper, we present an overview of industrial WNs (IWNs), discuss IWN features and related techniques, and then provide a new architecture based on quality of service and quality of data for IWNs. We also propose some applications for IWNs and IWN standards. Then, we will use a case from our previous achievements to explain how to design an IWN under Industry 4.0. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make IWNs truly ubiquitous for a wide range of applications.

A review of industrial wireless networks in the context of Industry 4.0

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks

Network lifetime plays an integral role in setting up an efficient wireless sensor network. The objective of this paper is twofold. The first one is to deploy sensor nodes at optimal locations such that the theoretically computed network lifetime is maximum. The second is to schedule these sensor nodes such that the network attains the maximum lifetime. Thus, the overall objective of this paper is to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, we use this knowledge to compute locations of deployment such that the network lifetime is maximum. Further, the nodes are scheduled to achieve this upper bound. In this paper, we use artificial bee colony algorithm and particle swarm optimization for sensor deployment problem followed by a heuristic for scheduling. A comparative study shows that artificial bee colony algorithm performs better for sensor deployment problem. The proposed heuristic was able to achieve the theoretical upper bound in all the experimented cases.

Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks

Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a low-cost and high-coverage-quality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and, therefore, increase hardware costs and create coverage holes. This paper presents the efficient obstacle-resistant robot deployment (ORRD) algorithm, which involves the design of a node placement policy, a serpentine movement policy, obstacle-handling rules, and boundary rules. By applying the proposed ORRD, the robot rapidly deploys a near-minimal number of sensor nodes to achieve full sensing coverage, even though there exist unpredicted obstacles with regular or irregular shapes. Performance results reveal that ORRD outperforms the existing robot deployment mechanism in terms of power conservation and obstacle resistance and, therefore, achieves better deployment performance.

Obstacle-Resistant Deployment Algorithms for Wireless Sensor Networks

This paper proposes a robot-deployment algorithm that overcomes unpredicted obstacles and employs full-coverage deployment with a minimal number of sensor nodes. Without the location information, node placement and spiral movement policies are proposed for the robot to deploy sensors efficiently to achieve power conservation and full coverage, while an obstacle surrounding movement policy is proposed to reduce the impacts of an obstacle upon deployment. Simulation results reveal that the proposed robot-deployment algorithm outperforms most existing robot-deployment mechanisms in power conservation and obstacle resistance and therefore achieves a better deployment performance.

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An Obstacle-Free and Power-Efficient Deployment Algorithm for Wireless Sensor Networks

In wireless sensor networks, a geographic region without the functionality of sensing and communication can generally be treated as an obstacle, which significantly impacts the performance of existing location-based routing. An obstacle can dynamically be formed due to unbalanced deployment, sensor failure, or power exhaustion, animus interference, or physical obstacles such as mountains or buildings. This paper proposes novel algorithms that enable the existing location-based routing protocols that resist obstacles. Applying the proposed active route-guiding protocol for single obstacles (S-RGP), border nodes that surround the obstacles will actively establish a forbidden region for concave obstacles and make the obstacle information transparent. Then, packets will be guided to overcome the obstacle and move along the shorter path from the encountered border node to the sink node. In addition, the proposed active route-guiding protocol for multiple obstacles (M-RGP) takes multiple obstacles into consideration and integrates their information to help the packets overcome multiple obstacles. Simulation results show that the proposed S-RGP and M-RGP create low overhead and significantly reduce the average route length, and, therefore, improve the energy consumption and end-to-end delay for a wireless sensor network with obstacles.

Active Route-Guiding Protocols for Resisting Obstacles in Wireless Sensor Networks

A novel multi-channel MAC protocol with directional antenna for enhancing spatial reuse and bandwidth utilization in WLANs

The k-barrier coverage problem is known as the problem of detecting the intruders by at least k sensors when the intruders moving along the crossing paths from one boundary to another. This paper proposes decentralized algorithms to cope with the k-barrier coverage problem. For a given value k, the proposed algorithms find out the maximum disjoint sets of sensors such that each set of sensors meets the requirement of k-barrier coverage for users. Three mechanisms, called Basic, Backtracking, and Branch, are proposed for constructing as more as possible the disjoint sets of sensors that satisfy the requirement of k-barrier coverage. Performance study reveals that the proposed algorithms achieve near-optimal performance.

Yu-Chieh Chen

Papers

Obstacle-Resistant Deployment Algorithms for Wireless Sensor Networks

An Obstacle-Free and Power-Efficient Deployment Algorithm for Wireless Sensor Networks

Active Route-Guiding Protocols for Resisting Obstacles in Wireless Sensor Networks

A novel multi-channel MAC protocol with directional antenna for enhancing spatial reuse and bandwidth utilization in WLANs

On-supporting energy balanced k-barrier coverage in wireless sensor networks