A Probabilistic Acknowledgment Mechanism for Wireless Sensor Networks

Basten

2015

ACM Trans. Sen. Netw.

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Wireless Sensor Networks (WSNs) are commonly deployed in dynamic environments where events, such as moving sensor nodes and changing external interference, impact the performance, or Quality-of-Service (QoS), of the network. QoS is expressed by the values of multiple, possibly conflicting, network quality metrics, such as network lifetime and maximum latency of communicating a packet to the sink. A sufficient QoS should be provided by the WSN to ensure that the end-user can successfully use the WSN to perform its application. Current run-time reconfiguration approaches optimize only a single network QoS metric, focus on node quality metrics and/or consider a homogenous configuration. They thereby ignore existing trade-offs between important network QoS metrics, the need for nodes to collaboratively achieve the network QoS or heterogeneity in the network. We propose a distributed reconfiguration approach that actively maintains a sufficient QoS at run-time for a heterogeneous WSN in a dynamic environment. To resolve differences between the current and required QoS of the network, nodes reconfigure themselves by adapting controllable parameters of the protocol stack, such as the transmission power or maximum number of packet retransmissions. They take the opportunities of other nodes affecting the same network QoS metric into account. The behaviour of the reconfiguration approach and the trade-offs involved are analyzed in detail. With the use of simulations and experiments with an actual deployment we show that our approach allows a better optimization of QoS objectives while constraints are met, e.g., it achieves the same packet-loss with a significant longer lifetime, compared to current (re-)configuration approaches.

Section: Experimental Analysismentioning

confidence: 99%

A Distributed Reconfiguration Approach for Quality-of-Service Provisioning in Dynamic Heterogeneous Wireless Sensor Networks

Steine

Basten

2015

ACM Trans. Sen. Netw.

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“…The MAC protocol we consider for all nodes is the default Low-Power-Listening MAC [9] available in TinyOS. We use an approach inspired by [1] where we always retransmit a packet a given number of times and not rely on the receiver to send acknowledgments. Static nodes use a fixed static node to route data to.…”

Section: Controller Performance Analysismentioning

confidence: 99%

A Distributed Feedback Control Mechanism for Quality-of-Service Maintenance in Wireless Sensor Networks

Steine

2012 15th Euromicro Conference on Digital System Design

Basten

2012

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Wireless sensor networks are typically operating in a dynamic context where events, such as moving sensor nodes and changing external interference, constantly impact the qualityof-service of the network. We present a distributed feedback control mechanism that actively balances multiple conflicting network-wide quality metrics, such as power consumption and end-to-end packet latency, for a heterogeneous wireless sensor network operating in a dynamic context. Nodes constantly decide if and how to adapt controllable parameters of the entire protocol stack, using sufficient information of the current network state. Using experiments with an actual deployment we show that our controller allows to maintain the required network-wide qualityof-service, with up to 30% less power consumed, compared to the most applicable (re-)configuration approaches. I. MOTIVATIONControllable parameters of individual sensor nodes in a Wireless Sensor Network (WSN), such as the radio transmission power, MAC protocol duty cycle and selected routing parent(s), determine the behaviour of the network. The values of these parameters, referred to as the configuration, determine to what extent expectations on quality metrics, i.e., the required Quality-of-Service (QoS), are satisfied. WSNs typically operate in a dynamic environment or exhibit dynamic behaviour themselves causing the configuration required to achieve sufficient QoS to vary over time. Statically configured nodes [5], [6] cannot cope with dynamics; run-time adaptation of parameters, or reconfiguration, is needed. Current approaches focus either on a single metric [4], [16], typically energy consumption, or on optimizing local metrics [13], [18] and often consider a homogenous network. In practice, the requirements of the applications are typically expressed by multiple conflicting quality metrics expressed on a networkwide scale [3], [14], such as a maximum end-to-end latency of 100 milliseconds, a packet delivery ratio of 90% or maximal network lifetime. There is only limited existing work on runtime adaptation techniques that consider multiple QoS metrics expressed on a network-wide scale. The recent work of [19] proposes a centralized approach which determines how to adapt MAC protocol parameters based on centrally collected network QoS information. In practice, centralized approaches might be too costly, non-scalable or not able to respond quick enough to dynamic events. By focusing on only the MAC protocol, it does not exploit the fact that the parameters from all protocols may influence the behaviour of the network [7].As a homogenous configuration is assumed where all nodes use the same MAC parameters, it furthermore ignores the typical heterogeneity in WSNs and resulting variation of the impact of nodes on the trade-offs involved.In this extended abstract, we introduce a distributed feedback control mechanism to maintain a required QoS, defined by multiple quality metrics, for a WSN in a dynamic context. The approach separates the adaptivity from protocol operation and lets...

“…Figure 6 gives an example. Assuming that links (4,5) and (4,11) are better than link (4,3), irrespective of the repositioning actions of sinks s 2 and s 3 , sink s 1 remains in the oscillating state (notice that the content of the set C 1 does not change, and only stochastic effects in the latency estimations may accidentally lead the algorithm out of the oscillating state). The sink nodes s 2 and s 3 get trapped in a small part of the network by the sink s 1 that has reached a local optimum.…”

Section: Initial Sink Deploymentmentioning

confidence: 99%

“…Network nodes are assumed to have a transmission range of √2R (i.e., central nodes have 8 neighbors on average). A stochastic radio model is used to determine packet reception ratios, [8], [5]. The packet reception ratio (reception probability) for nodes whose distance is less than half of the transmission range is assumed to be one, and it linearly drops to zero for nodes at a distance equal to the transmission range.…”

Section: A Simulation Setupmentioning

confidence: 99%

Fast sink placement for Gossip-based Wireless Sensor Networks

Blagojević

2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC)

Basten³

et al. 2012

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Abstract-In this paper we address the problem of sink placement for Gossip-based Wireless Sensor Networks (GWSN). Sink placement plays an important role in planning and deployment of sensor networks. It is an efficient means to improve performance and achieve design objectives. Sink deployment requires an optimization strategy to search a space of possible placement options, and a performance evaluation method to assess the quality of different sink placements. The stochastic nature of the gossip protocol makes this task challenging for GWSN. Simulation is the most common way to accurately evaluate gossiping performance; however, the time required to obtain statistically significant results is considerable and limits the scalability of the sink deployment process. We use a fast and accurate performance evaluation technique, which exploits specifics of the sink placement problem and significantly reduces evaluation time. In order to further improve the speed of the sink placement procedure we propose a greedy simulated annealing search heuristic that converges fast to a near-optimal placement. We have performed an extensive set of experiments to evaluate the performance of the proposed sink placement framework.