ShoX: An Easy to Use Simulation Platform for Wireless Networks

Lessmann, Johannes; Heimfarth, Tales; Janácik, Peter

doi:10.1109/uksim.2008.32

Cited by 54 publications

(14 citation statements)

References 1 publication

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“…They do not cover all aspects mentioned in the above description, but focus on the behavior of the system using the coordination strategy described in Section 6. These simulations were conducted using ShoX [26], a powerful wireless network simulator implemented in Java.…”

Section: Resultsmentioning

confidence: 99%

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

Freitas

Heimfarth

Allgayer

et al. 2010

INT J COMPUT COMMUN

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Abstract:Sensor networks are being used to implement different types of sophisticated emerging applications, such as those aimed at supporting ambient intelligence and surveillance systems. This usage is enhanced by employing sensors with different characteristics in terms of sensing, computing and mobility capabilities, working cooperatively in the network. However, the design and deployment of these heterogeneous systems present several issues that have to be handled in order to meet the user expectations. The main problems are related to the nodes' interoperability and the overall resource allocation, both inter and intra nodes. The first problem requires a common platform that abstracts the nodes' heterogeneity and provides a smooth communication, while the second is handled by cooperation mechanisms supported by the platform. Moreover, as the nodes are supposed to be heterogeneous, a customizable platform is required to support both resource rich and poorer nodes. This paper analyses surveillance systems based on a heterogeneous sensor network, which is composed by lowend ground sensor nodes and autonomous aerial robots, i.e. Unmanned Aerial Vehicles (UAVs), carrying different kinds of sensors. The approach proposed in this work tackles the two above mentioned problems by using a customizable hardware platform and a middleware to support interoperability. Experimental results are also provided.

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Section: Resultsmentioning

confidence: 99%

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

Freitas

Heimfarth

Allgayer

et al. 2010

INT J COMPUT COMMUN

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“…We have implemented OBM in ShoX [10], a wireless network simulator with a very modular architecture and strong visualization capabilities. Since we have described the routing in OBM already, for the simulation, we are particularly interested in the mobility -traffic integration.…”

Section: Resultsmentioning

confidence: 99%

An Integrated Node Behavior Model for Office Scenarios

Lessmann

Lutters

2008

41st Annual Simulation Symposium (Anss-41 2008)

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For realistic simulations of ad hoc networks, the movements of the participating nodes must be considered. Many models have been proposed which generate movement patterns based on nodes' roles and activities. However, previous models mostly consider node activities individually for each node, making it difficult to precisely model joint activities. While advanced previous models are either based on the notion of schedules or on state machines, we propose a hybrid approach which combines the flexibility of the latter with the ability of the former to express time constraints. Further, as opposed to previous work, our model allows nodes to assume multiple roles and provides an intuitive way to reconcile their potentially conflicting schedules and state machines. Our model also considers the on/off behavior of nodes which is very important to evaluate adaptivity of protocols to node joins and failures under realistic conditions. A major contribution of our work is further that we take into account the fact that mobility, on/off patterns and traffic generation in a network cannot be modeled independently. Our comprehensive model is the first to include all three parts in one consistent form. Finally, it introduces the notion of home and preferred positions which allows very precise modeling of the general node distribution and positioning in an office scenario.

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“…In order to assess the efficiency of our innovative protocol, we performed simulations in the GrubiX Wireless Network Simulator, a redesign and extension of ShoX [12]. For the simulations, we adopt a radio model based on the IEEE 802.15.4, fixed transmission power, bidirectional links, and Free Space propagation model for isotropic point source in an ideal propagation medium.…”

Section: A Simulation Setupmentioning

confidence: 99%

A Preamble Ahead Anycast Protocol for WSNs

Heimfarth

Giacomin

Araujo

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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This paper presents a novel cross-layer anycast medium access control (MAC) protocol for wireless sensor networks (WSNs) based on advanced preamble propagation and geographic routing. The protocol aims at reducing the sleepdelay problem, common in asynchronous preamble-based MAC protocols. When a packet is ready to be transmitted, differently from other state-of-the-art protocols, the preamble sequence is propagated in advance for several hops before packet release. So, a simultaneous propagation of signaling and packet is obtained. The number of nodes in the Forwarding Candidate Set (FCS) in our anycast protocol is chosen to settle the average speed of preambles to the velocity of packet propagation. In order to enable the simultaneous transmissions without collision, a minimum distance between preambles and data packet is preserved. Simulations results showed that our protocol outperforms in latency all other asynchronous MAC protocols appraised, for all evaluated packet sizes. This result was obtained with an energy consumption similar to the other state-of-the-art protocols.

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ShoX: An Easy to Use Simulation Platform for Wireless Networks

Cited by 54 publications

References 1 publication

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

An Integrated Node Behavior Model for Office Scenarios

A Preamble Ahead Anycast Protocol for WSNs

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