Bhaskar Krishnamachari

An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks

Summary form only given. In many sensor network applications the major traffic pattern consists of data collected from several source nodes to a sink through a unidirectional tree. We propose DMAC, an energy efficient and low latency MAC that is designed and optimized for such data gathering trees in wireless sensor networks. We first show that previously proposed MAC protocols for sensor networks that utilize activation/sleep duty cycles suffer from a data forwarding interruption problem, whereby not all nodes on a multihop path to the sink are notified of data delivery in progress, resulting in significant sleep delay. DMAC is designed to solve the interruption problem and allow continuous packet forwarding by giving the sleep schedule of a node an offset that depends upon its depth on the tree. DMAC also adjusts the duty cycles adaptively according to the traffic load in the network. We further propose a data prediction mechanism and the use of more-to-send (MTS) packets in order to alleviate problems pertaining to channel contention and collisions. Our simulation results show that by exploiting the application-specific structure of data gathering trees in sensor networks, DMAC provides significant energy savings and latency reduction while ensuring high data reliability.

Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks

We propose a distributed solution for a canonical task in wireless sensor networks - the binary detection of interesting environmental events. We explicitly take into account the possibility of sensor measurement faults and develop a distributed Bayesian algorithm for detecting and correcting such faults. Theoretical analysis and simulation results show that 85-95 percent of faults can be corrected using this algorithm, even when as many as 10 percent of the nodes are faulty.

Toward dynamic energy-efficient operation of cellular network infrastructure

The operation of cellular network infrastructure incurs significant electrical energy consumption. From the perspective of cellular network operators, reducing this consumption is not only a matter of showing environmental responsibility, but also of substantially reducing their operational expenditure. We discuss how dynamic operation of cellular base stations, in which redundant base stations are switched off during periods of low traffic such as at night, can provide significant energy savings. We quantitatively estimate these potential savings through a first-order analysis based on real cellular traffic traces and information regarding base station locations in a part of Manchester, United Kingdom. We also discuss a number of open issues pertinent to implementing such energy-efficient dynamic base station operation schemes, such as various approaches to ensure coverage, and interoperator coordination.

Energy-efficient forwarding strategies for geographic routing in lossy wireless sensor networks

Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-reception-within-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. We identify and illustrate this weak-link problem and the related distance-hop trade-off, whereby energy efficient geographic forwarding must strike a balance between shorter, high-quality links, and longer lossy links. The study is done for scenarios with and without automatic repeat request (ARQ). Based on an analytical link loss model, we study the distance-hop trade-off via mathematical analysis and extensive simulations of a wide array of blacklisting/link-selection strategies; we also validate some strategies using a set of real experiments on motes. Our analysis, simulations and experiments all show that the product of the packet reception rate (PRR) and the distance traversed towards destination is the optimal forwarding metric for the ARQ case, and is a good metric even without ARQ. Nodes using this metric often take advantage of neighbors in the transitional region (high-variance links). Our results also show that reception-based forwarding strategies are more efficient than purely distance-based strategies; relative blacklisting schemes reduce disconnections and achieve higher delivery rates than absolute blacklisting schemes; and that ARQ schemes become more important in larger networks.

Energy-latency tradeoffs for data gathering in wireless sensor networks

We study the problem of scheduling packet transmissions for data gathering in wireless sensor networks. The focus is to explore the energy-latency tradeoffs in wireless communication using techniques such as modulation scaling. The data aggregation tree - a multiple-source single-sink communication paradigm - is employed for abstracting the packet flow. We consider a real-time scenario where the data gathering must be performed within a specified latency constraint. We present algorithms to minimize the overall energy dissipation of the sensor nodes in the aggregation tree subject to the latency constraint. For the off-line problem, we propose (a) a numerical algorithm for the optimal solution, and (h) a pseudo-polynomial time approximation algorithm based on dynamic programming. We also discuss techniques for handling interference among the sensor nodes. Simulations have been conducted for both long-range communication and short-range communication. The simulation results show that compared with the classic shutdown technique, between 20% to 90% energy savings can be achieved by our techniques, under different settings of several key system parameters. We also develop an on-line distributed protocol that relies only on the local information available at each sensor node within the aggregation tree. Simulation results show that between 15% to 90% energy conservation can be achieved by the on-line protocol. The adaptability of the protocol with respect to variations in the packet size and latency constraint is also demonstrated through several run-time scenarios.

Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks

Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on/off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption.

An analysis of unreliability and asymmetry in low-power wireless links

Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.

Performance evaluation of the IEEE 802.15.4 MAC for low-rate low-power wireless networks

IEEE 802.15.4 is a new standard to address the need for low-rate low-power low-cost wireless networking. We provide in this paper one of the first simulation-based performance evaluations of the new medium access protocol in IEEE 802.15.4, focusing on its beacon-enabled mode for a star-topology network. We describe its key features such as the superframe structure, which allows devices to access channels in a contention access period (CAP) or a collision free period (CFP) and the beacon-based synchronization mechanism. Our performance evaluation study reveals some of the key throughput-energy-delay tradeoffs inherent in this MAC protocol. We provide an analysis comparing the energy costs of beacon tracking and non-tracking modes for synchronization, showing that the optimum choice depends upon the combination of duty cycles and data rates.

Delay efficient sleep scheduling in wireless sensor networks

Medium access techniques for wireless sensor networks raise the important question of providing periodic energy-efficient radio sleep cycles while minimizing the end-to-end communication delays. This study aims to minimize the communication latency given that each sensor has a duty cycling requirement of being awake for only 1/k time slots on an average. As a first step we consider the single wake-up schedule case, where each sensor can choose exactly one of the k slots to wake up. We formulate a novel graph-theoretical abstraction of this problem in the general setting of a low-traffic wireless sensor network with arbitrary communication flows and prove that minimizing the end-to-end communication delays is in general NP-hard. However, we are able to derive and analyze optimal solutions for two special cases: tree topologies and ring topologies. Several heuristics for arbitrary topologies are proposed and evaluated by simulations. Our simulations suggest that distributed heuristics may perform poorly because of the global nature of the constraints involved. We also show that by carefully choosing multiple wake-up slots for each sensor significant delay savings can be obtained over the single wake-up schedule case while maintaining the same duty cycling. Using this technique, we propose algorithms that offer a desirable bound of d+O(k) on the delay for specialized topologies like the tree and grid and a weaker guarantee of O((d+k)log n) for arbitrary graphs, where d is the shortest path between 2 nodes in the underlying topology and n is the total number of nodes.

Optimality of Myopic Sensing in Multichannel Opportunistic Access

This paper considers opportunistic communication over multiple channels where the state (ldquogoodrdquo or ldquobadrdquo) of each channel evolves as independent and identically distributed (i.i.d.) Markov processes. A user, with limited channel sensing capability, chooses one channel to sense and decides whether to use the channel (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a ldquogoodrdquo channel. The objective is to design a channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a partially observed Markov decision process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. This paper shows that a myopic policy that maximizes the immediate one-step reward is optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to two or three, while presenting a counterexample for the case of four channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and antijamming.