Proceedings of the workshop on Real-world wireless sensor networks - REALWSN '08

2008 ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real World ◽  
Wireless Sensor

scholarly journals Practical Data Prediction for Real-World Wireless Sensor Networks

2015 ◽  
Vol 27 (8) ◽  
pp. 2231-2244 ◽  
Author(s):  
Usman Raza ◽  
Alessandro Camerra ◽  
Amy L. Murphy ◽  
Themis Palpanas ◽  
Gian Pietro Picco
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real World ◽  
Wireless Sensor ◽  
Data Prediction

scholarly journals Power Grid-Oriented Cascading Failure Vulnerability Identifying Method Based on Wireless Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shudong Li ◽  
Yanshan Chen ◽  
Xiaobo Wu ◽  
Xiaochun Cheng ◽  
Zhihong Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Bearing Capacity ◽  
Real World ◽  
Wireless Sensors ◽  
Power Grid ◽  
Wireless Sensor ◽  
Cascading Failure ◽  
Cascade Effect ◽  
Network Power

In our paper, we study the vulnerability in cascading failures of the real-world network (power grid) under intentional attacks. Here, we use three indexes ( B , K , k -shell) to measure the importance of nodes; that is, we define three attacks, respectively. Under these attacks, we measure the process of cascade effect in network by the number of avalanche nodes, the time steps, and the speed of the cascade propagation. Also, we define the node’s bearing capacity as a tolerant parameter to study the robustness of the network under three attacks. Taking the power grid as an example, we have obtained a good regularity of the collapse of the network when the node’s affordability is low. In terms of time and speed, under the betweenness-based attacks, the network collapses faster, but for the number of avalanche nodes, under the degree-based attack, the number of the failed nodes is highest. When the nodes’ bearing capacity becomes large, the regularity of the network’s performances is not obvious. The findings can be applied to identify the vulnerable nodes in real networks such as wireless sensor networks and improve their robustness against different attacks.

scholarly journals Configuring the Topology of Wireless Sensor Networks against Node based Attacks

2019 ◽  
Vol 8 (3) ◽  
pp. 5671-5675
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Topology ◽  
Real World ◽  
Load Balance ◽  
Wireless Sensor ◽  
Critical Aspect ◽  
Robust Network ◽  
Node Failures

The design of topology of wireless sensor networks is critical aspect in real world networks. Configuring the network topology according the attacks of the network is the solution to several node based attacks. To counter those attacks, an approach is proposed for wireless sensor networks. We propose to use networks that are free to scale and configurable according to the priority of the node. The parameters like throughput, efficiency, load balance should be exploited to make sure the connectivity of the network. The node based attacks concentrating on functional nodes in the network should be addressed and transmission of data should not be halted even after multiple attacks. The robust network should be built by applying the strategies to counter the node failures and connectivity of the network remain unaffected.

scholarly journals Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks

2021 ◽  
Author(s):  
Haitham Afifi
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Real World ◽  
Autonomous Vehicles ◽  
Wireless Sensor ◽  
Acoustic Sensor Networks ◽  
Markov Decision ◽  
Multi Agent

<div>We develop a Deep Reinforcement Learning (DeepRL) based multi-agent algorithm to efficiently control</div><div>autonomous vehicles in the context of Wireless Sensor Networks (WSNs). In contrast to other applications, WSNs</div><div>have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the</div><div>quality of sensed data. Second, quality of service (QoS) which is used to measure the network’s performance. As</div><div>a use case, we consider wireless acoustic sensor networks; a group of speakers move inside a room and there</div><div>are microphones installed on vehicles for streaming the audio data. We formulate an appropriate Markov Decision</div><div>Process (MDP) and present, besides a centralized solution, a multi-agent Deep Q-learning solution to control the vehicles. We compare the proposed solutions to a naive heuristic and two different real-world implementations: microphones being hold or preinstalled. We show using simulations that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation and the proposed heuristic. Additionally, we provide theoretical analysis of the performance with respect to WSNs dynamics, such as speed, rooms dimensions and speaker’s talking time.</div>

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