Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

The problem of idle listening is one of the most significant sources of energy consumption in wireless sensor nodes, and many techniques have been proposed based on duty cycling to reduce this cost. In this paper, we present a new asynchronous duty cycle MAC protocol, called Receiver-Initiated MAC (RI-MAC), that uses receiver-initiated data transmission in order to efficiently and effectively operate over a wide range of traffic loads. RI-MAC attempts to minimize the time a sender and its intended receiver occupy the wireless medium to find a rendezvous time for exchanging data, while still decoupling the sender and receiver's duty cycle schedules. We show the performance of RI-MAC through detailed ns-2 simulation and through measurements of an implementation in TinyOS in a testbed of MICAz motes. Compared to the prior asynchronous duty cycling approach of X-MAC, RI-MAC achieves higher throughput, packet delivery ratio, and power efficiency under a wide range of traffic loads. Especially when there are contending flows, such as bursty traffic or transmissions from hidden nodes, RI-MAC significantly improves throughput and packet delivery ratio. Even under light traffic load for which X-MAC is optimized, RI-MAC achieves the same high performance in terms of packet delivery ratio and latency while maintaining comparable power efficiency.

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RI-MAC: a receiver-initiated asynchronous duty cycle MAC protocol for dynamic traffic loads in wireless sensor networks

Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

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Wireless Sensor Networks for Healthcare

Interest in Wireless Body Area Networks (WBANs) has increased significantly in recent years thanks to the advances in microelectronics and wireless communications. Owing to the very stringent application requirements in terms of reliability, energy efficiency, and low device complexity, the design of these networks requires the definition of new protocols with respect to those used in general purpose wireless sensor networks. This motivates the effort in research activities and in standardisation process of the last years. This survey paper aims at reporting an overview of WBAN main applications, technologies and standards, issues in WBANs design, and evolutions. Some case studies are reported, based on both real implementation and experimentation on the field, and on simulations. These results have the aim of providing useful insights for WBAN designers and of highlighting the main issues affecting the performance of these kind of networks.

A Survey on Wireless Body Area Networks: Technologies and Design Challenges

The wireless medium being inherently broadcast in nature and hence prone to interferences requires highly optimized medium access control (MAC) protocols. This holds particularly true for wireless sensor networks (WSNs) consisting of a large amount of miniaturized battery-powered wireless networked sensors required to operate for years with no human intervention. There has hence been a growing interest on understanding and optimizing WSN MAC protocols in recent years, where the limited and constrained resources have driven research towards primarily reducing energy consumption of MAC functionalities. In this paper, we provide a comprehensive state-of-the-art study in which we thoroughly expose the prime focus of WSN MAC protocols, design guidelines that inspired these protocols, as well as drawbacks and shortcomings of the existing solutions and how existing and emerging technology will influence future solutions. In contrast to previous surveys that focused on classifying MAC protocols according to the technique being used, we provide a thematic taxonomy in which protocols are classified according to the problems dealt with. We also show that a key element in selecting a suitable solution for a particular situation is mainly driven by the statistical properties of the generated traffic.

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MAC Essentials for Wireless Sensor Networks

In this paper we present X-MAC, a low power MAC protocol for wireless sensor networks (WSNs). Standard MAC protocols developed for duty-cycled WSNs such as BMAC, which is the default MAC protocol for TinyOS, employ an extended preamble and preamble sampling. While this "low power listening" approach is simple, asynchronous, and energy-efficient, the long preamble introduces excess latency at each hop, is suboptimal in terms of energy consumption, and suffers from excess energy consumption at nontarget receivers. X-MAC proposes solutions to each of these problems by employing a shortened preamble approach that retains the advantages of low power listening, namely low power communication, simplicity and a decoupling of transmitter and receiver sleep schedules. We demonstrate through implementation and evaluation in a wireless sensor testbed that X-MAC's shortened preamble approach significantly reduces energy usage at both the transmitter and receiver, reduces per-hop latency, and offers additional advantages such as flexible adaptation to both bursty and periodic sensor data sources.

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X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks

Phased array antennas enable the use of real-time beam-forming and null-steering to further increase control of signal strength and interference in wireless networks. Understanding the potential of this platform for both mesh and single-hop networks is becoming more important as smart antennas begin to appear in emerging networking standards. Prior attempts to test non-standard antenna platforms have typically focused around simulations, fixed (non-steerable) directional antenna testbeds, and small scale temporary setups utilizing 1 or 2 phased array antenna nodes over the span of a few hundred meters. This paper presents the challenges encountered – and solutions developed – in building WART, a permanent, campus-wide testbed for wireless networking with beam-forming antennas.

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Challenges in Deploying Steerable Wireless Testbeds

In wireless communication systems, advances in computer architecture and processor technology have made it possible for functionality previously implemented in hardware to become tunable via software. These software-defined radios (SDRs) will allow new radio devices to sense, reason, and adapt to changes in the RF environment and/or application requirements making them cognitive radios (CRs). Fully exploiting the flexibility of cognitive radios, however, requires an understanding of how different permutations of radio parameters impact application-specific performance metrics. For example, a CR that is not meeting its bit loss goals could change its operating frequency to reduce the impact of interference. However, the added overhead from changing frequencies could result in an application failing its latency requirements. This paper describes one such method for configuring a cognitive radio and demonstrates the efficacy of the technique on both a simulation based analysis and an in situ evaluation on a software radio platform. Our reconfiguration system quantifies the influence of radio parameters such as frequency agility, bit rate, and transmit power for adapting communication at the application, medium access control, and physical layers. The method calls for exhaustively evaluating a set of CR configurations against a variety of performance metrics and applying statistical processes to determine which settings will have the most significant impact on performance. Once this is done, the experimental results are then used to inform the design of an algorithm that is able to reconfigure to meet performance goals.

Implementation of a Reconfiguration Algorithm for Cognitive Radio

Mesh networks are always challenged for bandwidth since they use wireless connections for both access links and backhaul. This paper presents a methodology for studying wireless networking optimizations from the perspective of a network administrator: given a set of wireless optimization algorithms, how do we determine the best configuration of these algorithms? We not only attempt to find the best combination of channel assignment, association control, transmit power control, bit-rate adaptation and beam-form selection algorithms, but we are also the first to consider the order in which those distinct algorithms should be applied as a primary property. This paper addresses a centralized, measurement-driven optimization system used in simulation and a field deployment to administer these optimization configurations in an outdoor 802.11b WLAN featuring multiple, beam forming access points. The results should be applicable to either the backhaul, access or combined wireless links in a mesh. Aggregate throughput results are processed by a decision tree to classify optimization configurations into top and bottom tiers using pairwise ordering and algorithm selection attributes. The results demonstrate that: (1) the relative ranking of an algorithm from an optimization domain is dependent upon the combination and ordering in which it is applied and (2) the ordering of a set of algorithms is as significant to final performance as the combination selected.

The Effect of Ordering on Wireless Optimization Algorithms

The common practice in wireless networking optimization is to address a problem domain, such as channel assignment or transmit power control, and test it across various environments, topologies and traffic rates. This approach, however, provides only a limited context for researchers and administrators, who must decide not only the best combination, but also the best ordering of these independently derived and tested solutions.This work presents a system architecture and methodology for determining the best features of a configuration of optimizations. These techniques are used to evaluate the joint application of several optimization strategies from five optimization domains, including: channel assignment, association control, transmit power control, bit-rate adaptation and beam form selection. Results from the simulation and field deployment of a 45x45 meter outdoor WLAN show that for the tested scenario: (1) channel assignment should precede association control, (2) minimizing transmit power is ineffective, (3) greedy channel assignment outperforms the Hsum algorithm, (4) load balancing across APs is not significant and (5) null steering is ineffective.

Gary V. Yee

Papers

X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks

Challenges in Deploying Steerable Wireless Testbeds

Implementation of a Reconfiguration Algorithm for Cognitive Radio

The Effect of Ordering on Wireless Optimization Algorithms

Understanding the joint application of wireless optimizations