Next generation communication networks are moving towards autonomous wireless infrastructures, as for example, Wireless Sensor Networks (WSNs) that are capable of working unattended under dynamically changing conditions. Over the last few years, WSNs are being developed towards a large number of multimedia streaming applications, e.g., video surveillance, traffic control systems, health monitoring, and industrial process control. However, WSNs face important limitations in terms of energy, memory and computational power. The uncontrolled use of limited resources in conjunction with the unpredictable nature of WSNs in terms of traffic load injection, wireless link capacity fluctuations and topology modifications (e.g. due to node failures) may lead to congestion. Congestion can cause deterioration of the offered quality of service (QoS). This study proposes a bio-inspired congestion control approach for WSNs streaming applications that necessitate controlled performance with graceful degradation. The proposed approach prevents congestion by regulating the rate of each traffic flow based on the Lotka-Volterra competition model. Performance evaluations reveal that the proposed approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases.
TopIntroduction
Rapid technological advances and innovations in the area of autonomous systems push the vision of Ambient Intelligence from concept to reality. Networks of autonomous wireless sensor devices offer exciting new possibilities for achieving sensory omnipresence: small, (often) inexpensive, untethered sensor devices can observe and measure various environmental parameters, thereby allowing real-time and fine-grained monitoring of physical spaces around us. Wireless Sensor Networks (Akyildiz, 2002), can be used as platforms for health monitoring, battlefield surveillance, environmental observation, industrial control etc.
Despite the utmost importance of performance control, this issue has not been given enough attention from the research community. One of the main reasons is that research on WSNs has been heavily influenced by military applications in which dense, large-scale networks of sensors are expected to be deployed in a random manner, primarily with a view to tracking objects. However, most of the aforementioned critical applications necessitate small-scale networks with planned deployment of sensors close to selected locations/objects of interest in order to achieve controlled performance. The proposed approach is designed on the basis of providing performance control for critical applications in small-scale WSNs.
Typically, WSNs consist of small, cooperative devices (nodes) which may be constrained by computation capability, memory space, communication bandwidth and energy supply. However, with the rapid development of low-cost hardware CMOS cameras and microphones, autonomous sensor devices are becoming capable of ubiquitously retrieving multimedia content such as video and audio streams from the environment. This new and emerging type of sensor network, the so-called Wireless Multimedia Sensor Network (WMSN), has drawn the immediate attention of the research community (Akyildiz, 2007). As shown in Figure 1, a number of nodes that at a particular moment sense an event (grey-shaded nodes), can send streaming data through the network, in a multi-hop manner, to a dedicated sink node. Alternatively, some nodes may be constantly sending streaming data to the sink. The unpredictable nature of traffic load injected into the network as well as the uncontrolled use of the scarce network resources (buffer size, wireless channel capacity) are able to provoke congestion. Thus, there is an increased need to design novel congestion control strategies possessing self-* properties like self-adaptability, self-organization as well as scalability, and fairness, which are vital to the mission of dependable multimedia WSNs.
Figure 1. A WSN for wildlife monitoring
The focal point of this study is to propose a scalable and self-adaptive bio-inspired congestion control mechanism targeting streaming applications in WSNs for delivering enhanced application fidelity at the sink (in terms of packet delivery ratio and delay) under varying network conditions. More specifically, the main objective is to provide efficient and smooth rate allocation and control while maintaining fairness and friendliness with interfering flows. Biological processes which are embedded in decentralized, self-organizing and adapting environments, provide a desirable basis for computing environments that need to exhibit such properties.