Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks

We explore the following fundamental question - how fast can information be collected from a wireless sensor network? We consider a number of design parameters such as, power control, time and frequency scheduling, and routing. There are essentially two factors that hinder efficient data collection - interference and the half-duplex single-transceiver radios. We show that while power control helps in reducing the number of transmission slots to complete a convergecast under a single frequency channel, scheduling transmissions on different frequency channels is more efficient in mitigating the effects of interference (empirically, 6 channels suffice for most 100-node networks). With these observations, we define a receiver-based channel assignment problem, and prove it to be NP-complete on general graphs. We then introduce a greedy channel assignment algorithm that efficiently eliminates interference, and compare its performance with other existing schemes via simulations. Once the interference is completely eliminated, we show that with half-duplex single-transceiver radios the achievable schedule length is lower-bounded by max(2nk − 1,N), where nk is the maximum number of nodes on any subtree and N is the number of nodes in the network. We modify an existing distributed time slot assignment algorithm to achieve this bound when a suitable balanced routing scheme is employed. Through extensive simulations, we demonstrate that convergecast can be completed within up to 50% less time slots, in 100-node networks, using multiple channels as compared to that with single-channel communication. Finally, we also demonstrate further improvements that are possible when the sink is equipped with multiple transceivers or when there are multiple sinks to collect data

An Application-Tailored MAC Protocol for Wireless Sensor Networks

We describe a data management framework suitable for wireless sensor networks that can be used to adapt the performance of a medium access control (MAC) protocol depending on the query injected into the network. The framework has a\ud completely distributed architecture and only makes use of information available locally to capture information about network traffic patterns. It allows\ud nodes not servicing a query to enter a dormant mode which minimizes transmissions and yet maintain an updated view of the network. We then introduce an Adaptive, Information-centric and Lightweight MAC\ud (AI-LMAC) protocol that adapts its operation depending on the information presented by the framework. Our results demonstrate how transmissions are greatly reduced during the dormant mode. During the active mode, the MAC\ud protocol adjusts fairness to match the expected requirements of the query thus reducing latency. Thus such a data management framework allows the MAC to operate more efficiently by tailoring its needs to suit the requirements of the application

Design Aspects of An Energy-Efficient, Lightweight Medium Access Control Protocol for Wireless Sensor Networks

This document gives an overview of the most relevant design aspects of the lightweight medium access control (LMAC) protocol [16] for wireless sensor networks (WSNs). These aspects include selfconfiguring and localized operation of the protocol, time synchronization in multi-hop networks, network setup and strategies to reduce latency.\ud The main goal in designing a MAC protocol for WSNs is to minimize energy waste - due to collisions of messages and idle listening - , while limiting latency and loss of data throughput. It is shown that the LMAC protocol performs well on energy-efficiency and delivery ratio [19] and can\ud ensure a long-lived, self-configuring network of battery-powered wireless sensors.\ud The protocol is based upon scheduled access, in which each node periodically gets a time slot, during which it is allowed to transmit. The protocol does not depend on central managers to assign time slots to nodes.\ud WSNs are assumed to be multi-hop networks, which allows for spatial reuse of time slots, just like frequency reuse in GSM cells. In this document, we present a distributed algorithm that allows nodes to find unoccupied time slots, which can be used without causing collision or interference to other nodes. Each node takes one time slot in control to\ud carry out its data transmissions. Latency is affected by the actual choice of controlled time slot. We present time slot choosing strategies, which ensure a low latency for the most common data traffic in WSNs: reporting of sensor readings to central sinks

An efficient scalable scheduling mac protocol for underwater sensor networks

Underwater Sensor Networks (UWSNs) utilise acoustic waves with comparatively lower loss and longer range than those of electromagnetic waves. However, energy remains a challenging issue in addition to long latency, high bit error rate, and limited bandwidth. Thus, collision and retransmission should be efficiently handled at Medium Access Control (MAC) layer in order to reduce the energy cost and also to improve the throughput and fairness across the network. In this paper, we propose a new reservation-based distributed MAC protocol called ED-MAC, which employs a duty cycle mechanism to address the spatial-temporal uncertainty and the hidden node problem to effectively avoid collisions and retransmissions. ED-MAC is a conflict-free protocol, where each sensor schedules itself independently using local information. Hence, ED-MAC can guarantee conflict-free transmissions and receptions of data packets. Compared with other conflict-free MAC protocols, ED-MAC is distributed and more reliable, i.e., it schedules according to the priority of sensor nodes which based on their depth in the network. We then evaluate design choices and protocol performance through extensive simulation to study the load effects and network scalability in each protocol. The results show that ED-MAC outperforms the contention-based MAC protocols and achieves a significant improvement in terms of successful delivery ratio, throughput, energy consumption, and fairness under varying offered traffic and number of nodes