Onur Soysal

Data Salmon: a greedy mobile basestation protocol for efficient data collection in wireless sensor networks

Our work addresses the spatiotemporally varying nature of data traffic in environmental monitoring and surveillance applications. By employing a network-controlled mobile basestation (MB), we present a simple energy-efficient data collection protocol for wireless sensor networks (WSNs). In contrast to the existing MB-based solutions where WSN nodes buffer data passively until visited by an MB, our protocol maintains an always-on multihop connectivity to the MB by means of an efficient distributed tracking mechanism. This allows the nodes to forward their data in a timely fashion, avoiding latencies due to long-term buffering. Our protocol progressively relocates the MB closer to the regions that produce higher data rates and reduces the average weighted multihop traffic, enabling energy savings. Using the convexity of the cost function, we prove that our local and greedy protocol is in fact optimal.

Coordinated Locomotion and Monitoring Using Autonomous Mobile Sensor Nodes

Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is unbounded and the physical phenomenon to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomenon of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamically changing environment with nodes having limited sensing range and energy, and moving under distributed control. To address these challenges, we propose a new distributed algorithm, Causataxis, which enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. (In Latin, causa means motive/interest. A taxis (plural taxes) is an innate behavioral response by an organism to a directional stimulus. We use Causataxis to refer to an interest driven relocation behavior.) Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its biosystem inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a swarm-based algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis outperforms the swarm-based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.

A Singlehop Collaborative Feedback Primitive for Wireless Sensor Networks

To achieve scalability, energy-efficiency, and timeliness, wireless sensor network deployments increasingly employ in-network processing. In this paper, we identify singlehop feedback collection as a key building block for in-network processing applications, and introduce a basic singlehop primitive, pollcast. The key idea behind this primitive is to exploit the receiver-side collision detection information at the MAC-layer to speed-up collaborative feedback collection. Using pollcast, a node can get an affirmation about the existence of a node-level predicate P in its neighborhood in constant time by asking all nodes where P hold to reply simultaneously. We have implemented pollcast on Tmotes using Chipcon 2420 radio. Our results show that this primitive is indeed lightweight, resilient, and effective. Our paper is also the first time receiver-side collision detection is achieved in a practical manner for Chipcon 2420 radio.

Data spider: a resilient mobile basestation protocol for efficient data collection in wireless sensor networks

Traditional deployments of wireless sensor networks (WSNs) rely on static basestations to collect data. For applications with highly spatio-temporal and dynamic data generation, such as tracking and detection applications, static basestations suffer from communication bottlenecks and long routes, which cause reliability and lifetime to plummet. To address this problem, we propose a holistic solution where the synergy of the WSN and the mobile basestation improves the reliability and lifetime of data collection. The WSN component of our solution is a lightweight dynamic routing tree maintenance protocol which tracks the location of the basestation to provide an always connected network. The mobile basestation component of our solution complements the dynamic tree reconfiguration protocol by trailing towards the data generation, and hence, reducing the number of hops the data needs to travel to the basestation. While both protocols are simple and lightweight, combined they lead to significant improvements in the reliability and lifetime of data collection. We provide an analytical discussion of our solution along with extensive simulations.

Coordinated Locomotion of Mobile Sensor Networks

Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is open (i.e borderless) and the physical phenomena to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomena of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamic sensing environment while minimizing energy consumption. Another major challenge is to maintain network connectivity for each MSN node during relocations. To address these challenges, we propose a new distributed algorithm, Causataxis1, that enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. Causataxis achieves scalable control of the MSN via a backbone-tree infrastructure maintained over clusterhead nodes, and achieves agility via localized cluster formation and dissolution. Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its bio-system inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a custom tuned swarm algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis can outperform the swarm based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.

PowerNap: An energy efficient MAC layer for random routing in wireless sensor networks

Idle-listening is the biggest challenge for energy-efficiency and longevity of multihop wireless sensor network (WSN) deployments. While existing coordinated sleep/wakeup scheduling protocols eliminate idle-listening for simple traffic patterns, they are unsuitable to handle the complex traffic patterns of the random routing protocols. We present a novel coordinated sleep/wakeup protocol PowerNap, which avoids the overhead of distributing complex, large sleep/wakeup scheduling information to the nodes. PowerNap piggybacks onto the relayed data packets the seed of the pseudo-random generator that encodes the scheduling information, and enables any recipient/snooper to calculate its sleep/wakeup schedule from this seed. In essence, PowerNap trades off doing extra computation in order to avoid expensive control packet transmissions. We show through simulations and real implementation on TelosB motes that PowerNap eliminates the idle-listening problem efficiently and achieves self-stabilizing, low-latency, and low-cost relaying of data packets for random routing protocols.

TRANSACT: A Transactional Framework for Programming Wireless Sensor/Actor Networks

Effectively managing concurrent execution is one of the biggest challenges for future wireless sensor/actor networks (WSANs): for safety reasons concurrency needs to be tamed to prevent unintentional nondeterministic executions, on the other hand, for real-time guarantees concurrency needs to be boosted to achieve timeliness. We propose a transactional, optimistic concurrency control framework for WSANs that enables understanding of a system execution as a single thread of control, while permitting the deployment of actual execution over multiple threads distributed on several nodes. By exploiting the atomicity and broadcast properties of singlehop wireless communication, we provide a lightweight implementation of our transactional framework on the motes platform.

Optimistic Concurrency Control for multihop sensor networks

In this study, we provide a lightweight singlehop primitive, Read-All-Write-Self (RAWS), that achieves optimistic concurrency control. RAWS guarantees serializability, which simplifies implementation and verification of distributed algorithms, compared to the low level message passing model. We also present a self-stabilizing multihop extension of RAWS, called Multihop Optimistic Concurrency Control Algorithm (MOCCA), to address the challenges of multihop networks. We implement RAWS on motes and investigate the effects of message loss over this novel primitive.