The number of real-world applications that require QoS guarantees is constantly increasing and they often follow the publish/subscribe (pub/sub)messaging paradigm, which provides loosely coupled many-to-many communication. Many QoS-aware systems use overlay networks as they allow flexible routing. To provide QoS-aware pub/sub messaging in overlay networks, the messaging system should be adaptive to the changes in network conditions (such as delay and failures). However, many pub/sub systems depend on a flexed routing topology and it is costly to rebuild this topology in case of failures. This study seeks to address this challenge with Delay-Cognizant Reliable Delivery (DCRD), a novel and delay-aware dynamic routing algorithm to provide reliable message delivery for pub/sub overlay networks. For reliable message delivery, DCRD no longer uses a flxed routing topology. Instead, it dynamically switches among different links to bypass link failures and increase the chance to meet QoS requirement. Each node tries different neighboring nodes in an order that is mathematically proven to minimize the expected delay of packet delivery. With all possible neighboring nodes sorted this way, DCRD guarantees that packets are delivered as long as there exists a path between the publisher and subscriber and that the expected delay is minimized. DCRD is extensively evaluated in simulation with comparison to existing tree-based routing approaches as well as a multi path approach using different network topologies, delay constraints, and loss probabilities. Simulation results show that DCRD performs better than all the baselines, providing reliable message delivery and satisfying the delay requirement for more than 98% of messages when the link failure probability is 4% or less.

The imminent growth of user-centric, pervasive sensing environments promotes sink mobility in an increasing number of event-based, sensor network applications including rescue missions, intrusion detection, and smart buildings. In these settings, one of the most critical challenges toward supporting quality of service, is effective distributed congestion avoidance. Congestion control techniques have been proposed in sensor networks mostly in the context of a static sink. In our work, we study the problem of traffic management in the context of sensor networks with a mobile sink. Under sink mobility, various new challenges arise that need to be effectively addressed. Adaptation to sink mobility requires agile as well as effective load estimation techniques. In addition, unlike static networks, path reliability often fluctuates due to path reconfigurations. Thus, injecting traffic during transient periods of poor path quality might wastefully detain network resources. In this work, we first study the effect of sink mobility on traffic load in sensor networks. We then propose adaptive routing as well as load estimation techniques that effectively adapt to sink relocations. A novel aspect of our approach is that it jointly considers the network load as well as path quality variations to facilitate intelligent, mobility-adaptive rate regulation at the sources. We provide a thorough study of the trade-offs induced due to persistent path quality variations and conduct extensive real MICA2-based testbed experiments to study the performance of the sensor network under sink mobility.

In wireless sensor networks, multiple flows from data collecting sensors to an aggregating sink could traverse paths that are largely interference coupled. These interference effects manifest themselves as congestion, and cause the flows to experience high packet loss and arbitrary packet delays. This is particularly problematic in event-based sensor networks (such as those in disaster recovery missions) where some flows are of greater importance than others and require a higher fidelity in terms of packet delivery and timeliness. In this paper we present COMUT (COngestion control for MUlti-class Traffic), a distributed cluster-based mechanism for supporting multiple classes of traffic in sensor networks. COMUT is based on the self-organization of the network into clusters , each of which autonomously and proactively monitors congestion within its localized scope. The clusters then exchange appropriate information to facilitate system wide rate control where, each data source, depending on the relative importance of its data flow and the experienced congestion en route the sink, is coerced into controlling its rate. Our simulation results demonstrate that (i) our techniques are highly effective in dealing with multiple, interfering flows and in achieving high delivery ratios and low delays compared to traditional approaches, (ii) operate successfully over multiple underlying routing protocols, (iii) provide higher throughput to higher importance flows, (iv) are responsive to failures and, finally, (v) achieve substantial energy savings due to the considerable reduction in packet drops via the effective regulation of the network load.

The imminent growth of user-centric, pervasive sensing environments promotes sink mobility in an increasing number of event-based, sensor network applications including rescue missions, intrusion detection and smart buildings. In these settings, one of the most critical challenges toward supporting quality of service, is effective distributed congestion avoidance. Congestion control techniques have been proposed in sensor networks mostly in the context of a static sink. In our work, we study the problem of congestion avoidance in the context of sensor networks with a mobile sink. Under sink mobility, various new challenges arise that need to be effectively addressed. Adaptation to sink mobility requires agile as well as effective load estimation techniques. In addition, unlike static networks, path reliability often fluctuates due to path reconfigurations. Thus, injecting traffic during transient periods of poor path quality might wastefully detain network resources. Our approach jointly considers the network load and path quality variations to facilitate intelligent, mobility-adaptive rate regulation at the sources. We implemented our techniques over a Mica2 testbed and through extensive experimental results, we demonstrate the working and benefits of our approach.

In this work we propose a traffic management mechanism to support real-time flows in highly unpredictable sensor network environments. The mechanism is based on a joint traffic regulation and end-to-end scheduling approach; a traffic regulation component adjusts the incoming packet rate to implicitly control the channel load and intelligently rejects packets that are more likely to miss their deadline while a laxity-based scheduling component projects the packets' per-hop delay and compensates for network end-to-end delays. Thus, high success ratios without severely degrading the fidelity are achieved. Our mechanism attempts to maintain accuracy in a resource-efficient manner even under extremely unstable network conditions where delays are difficult to model and compute. Furthermore, the adoption of a component-based approach allows for substantial independence from both the MAC and routing layers. We thoroughly evaluate our mechanism and demonstrate its accuracy and performance merits

Wireless sensor network applications typically integrate, within the same network, a variety of sensing devices including those for imaging, sound and temperature. In these settings, multiple flows of packets with different requirements in terms of transmission rates, bandwidth and jitter demands may be initiated towards the sink. Uncontrolled introduction of traffic from sources can cause network overload in areas of the network where the paths of the different flows interfere with each other. Such interference effects may result in congestion which leads to high packet loss and excessive delays. In this paper, we present CoBRA, a framework which incorporates distributed, cluster-based mechanisms to address the problem of congestion by enforcing rate control, for supporting multiple classes of traffic in sensor networks. Towards this goal, CoBRA periodically estimates the collective traffic load and, based on the current conditions, allocates and adjusts rates to sources on per-cluster bases. While doing so, CoBRA takes into consideration interference effects and rate requirements of concurrent flows. We have applied two different rate allocation policies using our framework and, through extensive simulation results we demonstrate its feasibility, effectiveness and performance advantages over traditional approaches

Broadcasting is an ad hoc network critical function component, and, in many deployments, making it power efficient is extremely important. Current power adaptive approaches proposed for broadcasting can be grouped into (a) centralized or omniscient schemes, and (b) decentralized or localized schemes. Due to the absence of global information, localized algorithms may not produce optimal or near-optimal solutions. On the other hand, global knowledge is not typically available to nodes and may be extremely expensive to disseminate. We examine the importance of the lack of global information on the performance of localized approaches. We have performed extensive simulations and compare the performance of localized power adaptive broadcasting with the performance of the well-known broadcast incremental power (BIP), an omniscient algorithm. We analyze the behaviors of the two protocols and identify the reasons for the differences in behavior. We observe that while the global state does provide better performance in terms of energy efficiency, the localized scheme performs better in terms of the latency incurred in the broadcast. Based on the observed behavioral traits of the two protocols, we suggest changes by which the energy consumption with the localized scheme is reduced by as much as 20%, while incurring almost no penalty in terms of latency.