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.

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.

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.

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.

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.

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.

In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performance, as measured by these two metrics, change 1) with transition probabilities, 2) with number of simulation steps, and 3) with arena size, is studied.