Elsevier

Ad Hoc Networks

Volume 11, Issue 8, November 2013, Pages 2541-2555
Ad Hoc Networks

Energy and spectrum-aware MAC protocol for perpetual wireless nanosensor networks in the Terahertz Band

https://doi.org/10.1016/j.adhoc.2013.07.002Get rights and content

Abstract

Wireless NanoSensor Networks (WNSNs), i.e., networks of nanoscale devices with unprecedented sensing capabilities, are the enabling technology of long-awaited applications such as advanced health monitoring systems or surveillance networks for chemical and biological attack prevention. The peculiarities of the Terahertz Band, which is the envisioned frequency band for communication among nano-devices, and the extreme energy limitations of nanosensors, which require the use of nanoscale energy harvesting systems, introduce major challenges in the design of MAC protocols for WNSNs. This paper aims to design energy and spectrum-aware MAC protocols for WNSNs with the objective to achieve fair, throughput and lifetime optimal channel access by jointly optimizing the energy harvesting and consumption processes in nanosensors. Towards this end, the critical packet transmission ratio (CTR) is derived, which is the maximum allowable ratio between the transmission time and the energy harvesting time, below which a nanosensor can harvest more energy than the consumed one, thus achieving perpetual data transmission. Based on the CTR, first, a novel symbol-compression scheduling algorithm, built on a recently proposed pulse-based physical layer technique, is introduced. The symbol-compression solution utilizes the unique elasticity of the inter-symbol spacing of the pulse-based physical layer to allow a large number of nanosensors to transmit their packets in parallel without inducing collisions. In addition, a packet-level timeline scheduling algorithm, built on a theoretical bandwidth-adaptive capacity-optimal physical layer, is proposed with an objective to achieve balanced single-user throughput with infinite network lifetime. The simulation results show that the proposed simple scheduling algorithms can enable nanosensors to transmit with extremely high speed perpetually without replacing the batteries.

Introduction

Nanotechnology is providing a new set of tools to the engineering community to create nanoscale components with very specific functionalities, such as computing, data storing, sensing and actuation. Advanced nano-devices can be created by integrating several of these nano-components in a single entity. An early application of these nano-devices is in the field of nanosensing. Nanosensors take advantage of the unique properties of novel nanomaterials to detect new types of events at the nanoscale. WNSNs, i.e., networks of nanosensors, will enable advanced applications of nanotechnology in the biomedical field (e.g., intrabody health monitoring and drug delivery systems), in environmental research (e.g., agriculture plague and air pollution control), and in defense and military technology (e.g., surveillance against new types of biological and chemical attacks at the nanoscale).

The peculiarities of nanosensors introduce many challenges in the realization of WNSNs. On the one hand, the miniaturization of classical antennas to meet the size requirements of nanosensors would impose the use of very high operating frequencies (hundreds of Terahertz), which would limit the feasibility of WNSNs. To overcome this limitation, the use of graphene-based nano-antennas and nano-transceivers has been recently proposed [10], [23], [17], [25]. As a result, nanosensors are expected to communicate in the Terahertz Band (0.1–10 THz). The Terahertz Band suffers from a very high propagation loss, which drastically limits the communication range of nanosensors due to their expectedly very limited power and energy. At the same time, though, it provides a very large bandwidth, which can be used to develop simple but yet efficient modulation and medium sharing schemes.

On the other hand, the very limited amount of energy that nano-batteries can hold and the unfeasibility to manually recharge or replace them, have motivated the development of novel nanoscale energy harvesting systems [27], [6], [4]. Nanoscale power generators convert vibrational, fluidic, electromagnetic or acoustic energy into electrical energy. When using energy harvesting systems, the energy of nanosensors does not just decrease with time, but has both positive and negative fluctuations. Therefore, rather than minimizing the energy consumption, a communication system should optimize the use of the energy in the nano-battery by capturing its temporal fluctuations. Ultimately, WNSNs can achieve perpetual operation if the energy consumption process and the energy harvesting process are jointly optimized.

Due to the transmission at very high speed in the Terahertz Band and the expectedly very high number of nanosensors in WNSNs willing to simultaneously communicate, novel Medium Access Control (MAC) protocols are needed to regulate the access to the channel and to coordinate and synchronize the transmissions among nano-devices. Classical MAC protocols cannot directly be used in WNSNs because they do not capture (i) the limited processing capabilities of nanosensors, which requires the development of ultra-low-complexity protocols [2]; (ii) the peculiarities of the Terahertz Band [12], i.e., very large distant-dependent bandwidth (bandwidth is not a problem anymore, but synchronization is) and very high propagation loss (very limited transmission range); and, (iii) temporal energy fluctuations of nanosensors due to the behavior of power nano-generators [9]. Therefore, there is a need to revise the traditional assumptions in MAC design and propose new solutions tailored to this paradigm.

In this paper, we propose an energy and spectrum aware MAC protocol to achieve perpetual WNSNs. First, we propose to take advantage of the hierarchical network architecture of WNSNs and shift the complexity of the MAC protocol towards more resourceful nano-controllers. In our solution, the nano-controller regulates the access to the channel of the nanosensors, by following a Time Division Multiple Access (TDMA) approach. To guarantee a fair, throughput and lifetime optimal access to the channel, the nano-controller takes into account the data requirements and energy constraints of the different nanosensors willing to communicate. Moreover, this is done for two different possible physical layers, namely, a more practical physical layer based on a recent proposed pulse-based scheme for nanoscale communications, and a theoretical bandwidth-adaptive capacity-optimal physical layer. The system model and an overview of the proposed solution are explained in Section 3 and Section 4, respectively.

As the essential building block of the proposed MAC solution, the throughput-and-lifetime optimal schedule has to be designed, which aims to find an optimal transmission order for the nanosensors so that the network throughput is maximized, while maintaining the infinite network lifetime. Towards this end, we first derive an important system design parameter, namely, the critical packet transmission ratio (CTR). The CTR is the maximum allowable ratio between the transmission time and the energy harvesting time, below which the nanosensor node can harvest more energy than the consumed one, thus achieving perpetual data transmission. Thanks to the peculiarities of the Terahertz Band and the nanoscale energy harvesting process, it is revealed that the CTR exhibits a unique distance-dependent nature so that nanosensors at different locations possess different CTR. The definition and the details on the computation of the CTR are explained in Section 5.

Based on the CTR, a novel symbol-compression based MAC solution, built on the pulse-based physical layer, is introduced. The symbol-compression solution utilizes the unique elasticity of the inter-symbol spacing to allow multiple nanosensors to transmit their packets in parallel without inducing any transmission collisions. Based on this symbol-compression solution, a sub-optimal symbol-compression scheduling algorithm is proposed, which can assign each nanosensor with different sets of transmission slots in such a way that all nanosensors achieve their near-maximum single-user throughput, simultaneously, while maintaining their transmission ratios below the CTR for energy balancing. Different from the pulse-based physical layer, we reveal that there exist three unique properties of the capacity-optimal physical layer, namely, (i) non-overlapped packet transmissions, (ii) nonexistence of throughput-and-lifetime optimal schedules, and (iii) the single-user throughput unbalance. Then, based on these properties, a packet-level timeline scheduling algorithm is proposed to achieve the balanced single-user throughput with the infinite network lifetime. The algorithms are presented in Section 6.

The remainder of this paper is organized as follows. In Section 2, we review the recent literature related to MAC protocols for WNSNs. In Section 3, we describe the nanosensor model and network model used in our analysis. In Section 4, we provide an overview of the proposed energy and spectrum-aware MAC protocol. In Section 5, we analytically obtain the energy harvesting rate and the energy consumption rate for the two proposed physical layers, and compute the CTR. We present the optimal throughput and lifetime scheduling algorithms in Section 6 and evaluate their performance in Section 7. Finally, we conclude the paper in Section 8.

Section snippets

Related work

There are not many MAC solutions for WNSNs for the time being. In [13], we proposed the PHLAME, the first MAC protocol for ad hoc nanonetworks. In this protocol, nano-devices such as nanosensors are able to dynamically choose different physical layer parameters based on the channel conditions and the energy of the nano-devices. These parameters were agreed between the transmitter nano-device and the receiver nano-device by means of a handshaking process. However, there are two limitations in

System model

In this section, we summarize the main peculiarities of nanosensors that affect the design of protocols for WNSNs as well as the envisioned network model of WNSNs.

Overview of energy and spectrum-aware MAC

The Terahertz Band supports the transmission at very high bit-rates. MAC protocols that involve heavy signaling (e.g., classical handshaking process with conventional RequestToSend and ClearToSend packet exchange) may limit the achievable throughput of Terahertz Band communication networks, especially if an external device, in this case the nanocontroller, can take care of the synchronization among nano-devices. For this, we propose a dynamic scheduling scheme based on TDMA, which is tailored

Critical packet transmission ratio

Since all the nanosensors should directly communicate with the nanocontroller, to achieve the above design objectives (not to consume more energy than the available one and make sure a fair use of the resources among nanosensors), we need first to derive the critical packet transmission ratio (CTR) at different distances. The CTR is defined as the ratio between the duration of the transmission slot and the total duration of the transmission timeslot and the sleeping timeslot. Below this CTR,

Throughput-and-lifetime optimal scheduling

As introduced in the previous section, the critical packet transmission ratio determines the percentage of time a nanosensor can transmit so that it can ensure the balance between the harvested and the consumed energies. In this section, based on this CTR, we study the throughput-and-lifetime optimal scheduling problem, which aims to find an optimal transmission schedule for the nanosensors so that the network throughput is maximized, while maintaining the infinite network lifetime. To this

Performance evaluation

In this section, we evaluate the performance of the proposed scheduling algorithms under the pulse-based physical layer and the capacity optimal physical layer, respectively. Specifically, we first analyze the critical transmission ratio and the maximum single-user throughput of the above mentioned two communication schemes. Then, we study the actual single-user throughput under the proposed scheduling algorithms, which approaches the maximum one.

In our numerical analysis, we use the following

Conclusions

WNSNs will boost the applications of nanotechnology in many fields of our society, ranging from healthcare to homeland security and environmental protection. However, enabling the communication in WNSNs is still an unsolved challenge. We acknowledge that there is still a long way to go before having autonomous nanosensors, but we believe that hardware-oriented research and communication-focused investigations will benefit from being conducted in parallel from an early stage. In this paper, we

Pu Wang received the B.S. degree in Electrical Engineering from the Beijing Institute of Technology, Beijing, China, in 2003 and the M.Eng. degree in Computer Engineering from the Memorial University of Newfoundland, St. Johns, NL, Canada, in 2008. He received the Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology, Atlanta, GA, in 2013. He is currently an Assistant Professor with the Department of Electrical Engineering and Computer Science, Wichita

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    Pu Wang received the B.S. degree in Electrical Engineering from the Beijing Institute of Technology, Beijing, China, in 2003 and the M.Eng. degree in Computer Engineering from the Memorial University of Newfoundland, St. Johns, NL, Canada, in 2008. He received the Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology, Atlanta, GA, in 2013. He is currently an Assistant Professor with the Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS. He was named BWN Lab Researcher of the Year 2012, Georgia Institute of Technology. He received the TPC top ranked paper award of IEEE DySPAN 2011. He was also named Fellow of the School of Graduate Studies, 2008, Memorial University of Newfoundland. He is a member of IEEE. His research interests include wireless sensor networks, cognitive radio networks, nanonetworks, multimedia communications, wireless communications in challenged environment, Internet of things, and cyber-physical systems.

    Josep Miquel Jornet received the Engineering Degree in Telecommunication and the Master of Science in Information and Communication Technologies from the Universitat Politcnica de Catalunya, Barcelona, Spain, in 2008. He received the Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology, Atlanta, GA, in 2013, with a fellowship from “la Caixa” (2009–2010) and Fundacin Caja Madrid (2011–2012). He is currently an Assistant Professor with the Department of Electrical Engineering at the University at Buffalo, The State University of New York. From September 2007 to December 2008, he was a visiting researcher at the Massachusetts Institute of Technology, Cambridge, under the MIT Sea Grant program. He was the recipient of the Oscar P. Cleaver Award for outstanding graduate students in the School of Electrical and Computer Engineering, at the Georgia Institute of Technology in 2009. He also received the Broadband Wireless Networking Lab Researcher of the Year Award at Georgia Institute of Technology in 2010. He is a member of the IEEE and the ACM. His current research interests are in electromagnetic nanonetworks, graphene-enabled wireless communication, Terahertz Band communication networks and the Internet of Nano-Things.

    M.G. Abbas Malik received the Ph.D. in Computer Science degree from University of Grenoble I, France and Master in Computational Linguistics from University of Paris 7 – Denis Didrot, Fance in 2010 and 2006 respectively. He also received the Mater in Computer Science degree from Punjab University College of Information Technology, University of the Punjab, Pakistan in 2003. He served as Assistant Professor in COMSATS Institute of Information Technology Lahore, Pakistan for academic year 2010–2011. Since September 2011, he has been working as Assistant Professor in Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

    Nadine Akkari received the B.S and the M.S. degrees in Computer Engineering from University of Balamand, Lebanon, in 1997 and 1999, respectively. She received the Master degree in Networks of Telecommunications from Saint Joseph University and the Faculty of Engineering of the Lebanese University, Lebanon, in 2001 and Ph.D. degree in Networking from National Superior School of Telecommunications (ENST), France, in 2006. She is currently an assistant professor with the faculty of Computing and Information Technology at King Abdulaziz University, Jeddah, Saudi Arabia. She is a member of IEEE. Her research interests are in wireless networks, mobility management protocols, and nanonetworks.

    Ian F. Akyildiz received the B.S., M.S., and Ph.D. degrees in Computer Engineering from the University of Erlangen-Nrnberg, Germany, in 1978, 1981 and 1984, respectively. Currently, he is the Ken Byers Chair Professor in Telecommunications with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, the Director of the Broadband Wireless Networking (BWN) Laboratory and the Chair of the Telecommunication Group at Georgia Tech. Since 2013, he is a FiDiPro Professor (Finland Distinguished Professor Program (FiDiPro) supported by the Academy of Finland) in the Department of Electronics and Communications Engineering, at Tampere University of Technology, Finland, and the founding director of NCC (Nano Communications Center). Since 2011, he is a Consulting Chair Professor at the Department of Information Technology, King Abdulaziz University (KAU) in Jeddah, Saudi Arabia. Since 2008, he is also an honoraryprofessor with the School of Electrical Engineering at Universitat Politcnica de Catalunya (UPC) in Barcelona, Catalunya, Spain and the founding director of N3Cat (NaNoNetworking Center in Catalunya). He is the Editor-in-Chief of Computer Networks (Elsevier) Journal, and the founding Editor-in-Chief of the Ad Hoc Networks (Elsevier) Journal, the Physical Communication (Elsevier) Journal and the Nano Communication Networks (Elsevier) Journal. He is an IEEE Fellow (1996) and an ACM Fellow (1997). He received numerous awards from IEEE and ACM. His current research interests are in nanonetworks, Long Term Evolution Advanced (LTE-A) networks, cognitive radio networks and wireless sensor networks.

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    This paper was funded by King Abdulaziz University, under Grant No. (11-15-1432/HiCi). The authors, therefore, acknowledge technical and financial support of KAU.

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