Swarms and Swarm Intelligence

Hinchey, Michael G.; Sterritt, Roy; Rouff, Christopher

doi:10.1109/mc.2007.144

Cited by 151 publications

(80 citation statements)

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“…It is a general term that can be applied to any animal that swarms. The term applies particularly to insects; hive or nesting organizations such as ants or bees are the most familiar pattern for swarming [17]. Swarming becomes an interesting research area where the phenomena are utilized to perform useful tasks in all fields.…”

Section: Swarmingmentioning

confidence: 99%

Swarm Flooding Attack against Directed Diffusion in Wireless Sensor Networks

Abuhaiba¹,

Hubboub²

2012

IJCNIS

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-The objective of this paper is to study the vulnerabilities of sensor networks, design, and implement new approaches for routing attack. As one of the cornerstones of network infrastructure, routing systems are facing more threats than ever; they are vulnerable by nature and challenging to protect. We present a new attack, Swarm Flooding Attack, against Directed Diffusion based WSNs, which targets the consumption of sensors computational resources, such as bandwidth, disk space, or processor time. Two variants of swarm attack have been introduced: Bee and Ant. Both approaches are inspired from the natural swarming difference between bees and ants. In all cases, the strategy used to mount an attack is the same. An attack consists of a set of malicious user queries represented by interests that are inserted into the network. However, the two forms of attack vary in the synchronization aspects among attackers. These types of attacks are hard to defend against as illustrated. For each of the proposed attack models, we present analysis, simulation, and experimental measurements. We show that the system achieves maximal damage on system performance represented by many metrics.Index Terms -wireless sensor network, denial of service attack, directed diffusion, swarming, flooding

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Section: Swarmingmentioning

confidence: 99%

Swarm Flooding Attack against Directed Diffusion in Wireless Sensor Networks

Abuhaiba¹,

Hubboub²

2012

IJCNIS

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“…The emergence of complex, or macroscopic, behaviors and the ability to achieve significant results as a team result from combining simple, or microscopic, behaviors. [HSR07] Implementation of the microscopic behavior ("swarm logic") from the definition often takes the form of a simple set of rules. Such simplicity keeps software development and maintenance costs down compared to other software-based autonomous control systems, and ease efforts to formally prove system behavior [Clo03].…”

Section: Autonomous Control Techniques and Uav Swarmsmentioning

confidence: 99%

A novel communications protocol using geographic routing for swarming UAVs performing a Search Mission

Lidowski

Mullins

Baldwin

2009

2009 IEEE International Conference on Pervasive Computing and Communications

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This research develops the UAV Search Mission Protocol (USMP) for swarming UAVs and determines the protocol's effect on search mission performance. It is hypothesized that geographically routing USMP messages improves search performance by providing geography-dependent data to locations where it impacts search decisions. It is also proposed that the swarm can use data collected by the geographic routing protocol to accurately determine UAV locations and avoid sending explicit location updates. USMP significantly improves mission performance over scenarios without inter-UAV communication. However, protocol designs that simply broadcast messages improve search performance by 83% in total searches and 20% in distance traveled compared to geographic routing candidates. Additionally, sending explicit location updates generates 3%-6% better performance per metric versus harvesting GPSR's location information.iv To my Wife and Son

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“…A basic PSO procedure, adapted from (Hu et al, 2003), is showed in code fragment 3. PSOs have been used to solve a wide variety of problems (Hinchey et al, 2007). The original PSO is intended to work on continuous spaces, and velocity is computed for each dimension x i ∈ x .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%