Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Asynchronous Coordination Techniques

  • Ruoyu Su
  • Dengyin ZhangEmail author
  • Ramachandran Venkatesan
  • Cheng Li
  • Zijun Gong
  • Fan Jiang
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_235-1

Synonyms

Definition

Asynchronous coordination technique is a communication approach that sensor nodes use different predetermined wake-up and sleep patterns for each cycle of network operations in order to achieve high energy efficiency and to guarantee network connectivity based on certain properties in linear algebra and optimization. No time synchronization is required in the network operations.

Historical Background

Underwater acoustic sensor networks (UWSNs) have attracted much research interest in recent years due to their wide range of potential applications such as marine environmental monitoring, undersea resources exploration, disaster monitoring and prevention, assisted location and navigation, and security monitoring (Akyildiz et al. 2015). Early generation of UWSNs include the Acoustic Local Area Network (ALAN), which was deployed by the Woods Hole Oceanographic Institution (WHOI) in 1994 of the coast of Monterey, California (Catipovic et al. 1993). ALAN, aiming at long-term data acquisition and ocean monitoring, consists of a number of sensor nodes placed on the seafloor and one surface node attached to a buoy. The underwater sensor nodes located on the seafloor transmit data to the surface node via acoustic links. Data was transmitted to the onshore base station via a radio frequency link. Another example of UWSNs is Seaweb 2000 (Rice et al. 2000), which was one of the first multi-hop underwater acoustic networks. This project advanced the underwater communication technology to achieve near-realtime, synoptic observation of the underwater environment. In Seaweb 2000, 14 sensor nodes (to sense, collect, and relay data) were placed on the seafloor and three gateway nodes with buoys (to collect data from sensor nodes and transmit data the onshore station) were deployed on the sea surface (Rice et al. 2002). A fundamental architecture of UWSNs is presented in Fig. 1. As shown in Fig. 1, underwater acoustic sensor nodes either directly report data to the surface node or transmit data to the sink node via multi-hop. The sink node aggregates data from different sensor nodes and then transmits to the surface node. The onshore base station will receive data from the satellite after the surface node reports collected data to the satellite.
Fig. 1

The fundamental architecture of UWSNs

Underwater acoustic communications consume more energy especially for long-range transmission (Zhang et al. 2015) compared with terrestrial communications. Furthermore, energy availability is limited in underwater acoustic communications because of the unchangeable and nonchargeable batteries in the sensor nodes. Frequently replacing sensor nodes generates high deployment cost, which is not realistic in long-term marine monitoring applications (Heidemann et al. 2012; Zhang et al. 2015). Therefore, unlike the high requirements of network throughput and packet delay, energy efficiency is a fundamental and significant issue when designing underwater network protocols.

An effective approach to energy saving is the wake-up coordination scheme. In the wake-up coordination scheme, each sensor node works periodically and has two modes of operations in each period: wake-up mode and sleep mode. In the wake-up mode, the transceiver is turned on for neighboring node discovery and data exchange (once the communication between a source node and destination node is established), whereas it remains off during the sleep mode so that energy consumption can be reduced. In general, the wake-up coordination scheme can be categorized into on-demand, synchronous, and asynchronous wake-up coordination schemes.

On-demand Wake-up Coordination Schemes

On-demand wake-up coordination scheme is a communication approach that each sensor node is equipped with a low power radio module to wake up the node for communication. The wake-up time can be predetermined according to application requirements, network data traffic, and so on. For example, cooperative underwater multichannel media access control (MAC) protocol (CUMAC) is a typical protocol based on the on-demand wake-up coordination mechanism, which adopts two acoustic transceivers to be equipped for one sensor node (Zhou et al. 2012). The low-priced acoustic transceiver is used to detect the channel by sending and receiving tone signal. The other acoustic transceiver is for data transmission. The tone pulse sent by the low-price acoustic transceiver at a predetermined time arrives at different sensor nodes at different times. Sensor nodes can catch the tone pulse by the low-price acoustic transceiver at the right time and then wake up the acoustic transceiver to exchange data packets at proper time. Similar mechanisms can be referred in Syed et al. (2008) and Jurdak et al. (2010).

Synchronous Wake-up Coordination Schemes

Synchronous wake-up coordination scheme is a communication approach that all sensor nodes are time synchronized before data transmission so that each sensor node can wake up at the proper time to send or receive data. Also, sensor nodes can turn off their transceivers to conserve energy when there is no data to transmit or receive. For example, underwater wireless acoustic networks MAC protocol (UWAN-MAC) is a distributed scalable energy-efficient scheme based on synchronous wake-up coordination mechanism (Min and Rodoplu 2008). In the UWAN-MAC protocol, before data transmission, each sensor node broadcasts signaling packets (i.e., time stamp) which includes information about the amount of time in sleep mode during one cycle and records information transmitted by its neighboring nodes to achieve time synchronization among all sensor nodes. The time stamps guarantee that neighboring nodes would wake up at the correct time in order to receive data packets without the knowledge of the propagation delay. Similar protocols can be found in van Dam and Langendoen (2003) and Ye et al. (2004).

Asynchronous Wake-up Coordination Schemes

Unlike the on-demand and synchronous wake-up coordination schemes, asynchronous wake-up coordination schemes utilize different predetermined wake-up and sleep patterns for each cycle of network operations to achieve high energy efficiency and to guarantee network connectivity without time synchronization during the network operations. By using asynchronous wake-up coordination schemes, two sensor nodes are able to communicate with each other at least once in one cycle if they are in the communication range. Meanwhile, sensor nodes can sleep as long as possible to reduce energy consumption when they do not have data to transmit and to forward. In general, each sensor node works periodically. In one period, the operations of each sensor node alternate between the wake-up mode and sleep mode. In the wake-up mode, the transceiver of the sensor node is turned on for neighboring node discovery and data exchange (once the communication between a source node and destination node is established). In the sleep mode, the transceiver of the sensor node remains off so that energy consumption can be reduced. Both the wake-up mode and the sleep mode may contain different numbers of time slots. A time slot is called an active slot when the sensor node operates in the wake-up mode and is called an inactive slot when the sensor node stays in the sleep mode. In this case, the operation status of a sensor node alternates between a predetermined number of active slots and inactive slots. To understand the diverse asynchronous wake-up coordination schemes, the following definitions and theorems are presented.

Preliminaries

Definition 1

(Quorum system) (Jiang et al. 2005) Let U be a universal set and U = {0,1,2,…,n – 1}. A quorum system Q is defined as a collection of non-empty subsets of U (e.g., G and H), which satisfies
$$ \forall G,H\in Q,G\cap H\ne \emptyset. $$
(1)

For example, U = {0,1,2}, Q = {{0,1}, {0,2}, {1,2}} is a quorum system under U.

Definition 2

(Rotational closure property) (Jiang et al. 2005) A quorum system has the rotation closure property when
$$\begin{aligned} &\forall G,H\in Q,i\in \left\{0,1,2,\dots, n-1\right\}\!,\\&\quad G\cap rotate\left(H,i\right)=\emptyset, \end{aligned}$$
(2)
where i is a nonnegative integer and rotate (H, i) = {(j + i) mod n|j ϵ H}.

For example, the quorum system Q = {{0,1}, {0,2}, {1,2}} under {0,1,2} has the rotation closure property. However, the quorum system Q′ = {{0,1}, {0,2}, {0,3}, {1,2,3}} under U = {0,1,2,3} has no rotation closure property because {0,1} ∩ rotate ({0,3}, 3) = Ø (Jiang et al. 2005).

Definition 3

(Zv, +) (Stinson 2004) (Zv, +) is a finite group of order v, where is v is a positive integer.

Definition 4

(Cyclic difference set) (Stinson 2004; Choi and Shen 2011) Given a set D with v elements and k subset elements, D : a1, a2, ⋯, ak(mod v) is called a (v,k,λ) – cyclic difference set in (Zv, +) if for every d ≠ 0 (mod v) there are exactly λ ordered pairs (ai, aj), such that ai – aj = d(mod v).

For example, D = {1,2,4} is called a (7,3,1)–cyclic difference set in (Z7, +).

Theorem 1

(Singer difference set) (Stinson 2004; Choi and Shen 2011) Let q be a prime power. Then there exists a (q2 + q + 1, q + 1, 1)–cyclic difference set in\( \left({Z}_{q^2+q+1},+\right) \).

The cyclic difference sets can be constructed according to Theorem 1. For instance, (7,3,1)–cyclic difference set when q = 2, (13,4,1)–cyclic difference set when q = 3, (21,5,1)–cyclic difference set when q = 4. More cyclic difference sets can be found in Appendix A of Stinson (2004).

Theorem 2

(Multiplier theorem) (Stinson 2004) Suppose there exists a (v,k,λ)–difference set D in an Abelian group (Zv, +) of order v. p is a multiplier of D if the following four conditions are satisfied: (1) p is prime; (2) gcd(p, v) = 1, gcd is greatest common divisor; (3) k – λ = 0(mod p); and (4) p > λ.

According to Theorems 1 and 2, p = 2 is a multiplier of (21,5,1)–cyclic difference set because 2 satisfies the four conditions in Theorem 2. Based on the multiplier, it is easy to construct two (7,3,1)–cyclic difference sets: {1,2,4} and {3,5,6}.

Quorum-Based Energy Conserving (QEC) Protocol

Figure 2 presents the basic idea of network operations in QEC. The white cube represents an inactive slot and the black and gray cube are an active slot. When node u and node v each arbitrarily choose a row and a column from a square of size n, respectively, their active intervals would overlap for at least two active slots, e.g., the 10-th and the 34-th slots shown in this figure (n = 7). Furthermore, there exists at least one overlapped active slot if two sensor nodes adopt two different grid sizes (Chao et al. 2006). It is obvious that a large value of n can lead to better energy conservation (larger number of time slots in one period associated with larger number of inactive slots) but it would also increase the latency. The QEC (Chao et al. 2006) accommodates different traffic loads by varying the value of n and thus achieve a balance between energy efficiency and packet delay. The simulation results show that QEC can outperform IEEE 802.11 in terms of energy consumption.
Fig. 2

The fundamental idea of QEC

Cyclic Difference Set (CDS)-Based Protocol

The CDS-based protocol determines the number and positions of active and inactive slots in one cycle using as Singer difference set of a (v, k, λ)–cyclic difference set, where v, k, and λ represent the total number of slots, the number of active slots, and the minimum number of overlapping active slots in one cycle, respectively (Zheng et al. 2003, 2006). The CDS-based protocol guarantees at least one active overlapping active slot between any two sensor nodes for any cyclic shift.

Figure 3 presents an asynchronous wake-up coordination schemes followed (7,3,1)–cyclic different set. In one period, a sensor node has seven time slots including three active slots and four inactive slots. The positions of active slots (i.e., black cubes) and inactive slots (i.e., white cubes) are determined by the (7,3,1)–cyclic different set. There exists at least one active overlapping slot between any two cycle shift for the cases of perfect and no alignment of time slot boundaries Zheng et al. (2006). That is, the network connectivity can be guaranteed and the energy consumption on idle listening is reduced as well. Furthermore, the CDS-based protocol conserves more energy on idle listening than that of the QEC protocol due to the minimum number of overlapping active slots is one between any cycle shift.
Fig. 3

An example of the network operations in the CDS-based protocol followed (7,3,1)–cyclic different set

Fig. 4

Extension of the CDS-based protocol by permutation and combination

Extension of CDS-Based Protocols

The extension of CDS-based protocols aims to reduce more energy consumption by leveraging a longer cycle period with less active slots without sacrificing network connectivity.

Exponential adaptive CDS-based protocol (EACDS) and multiplicative adaptive CDS-based protocol (MACDS) are the two extensions of the CDS-based protocols (Choi and Shen 2011). The key idea of these two schemes is the use of hierarchical arrangements of sets with the Kronecker product (Anderson 1998; Bernstein 2008). The EACDS-based scheme utilized a difference set scaled by another difference set, which is called the exponential set. The scaling was done by the Kronecker product and guaranteed that there was at least one overlapping slot between any two EACDSs with different duty cycles. For example, a difference set (vI, kI, λΙ), called an initial set, is scaled by another difference set (vE, kE, λΕ), called an exponential set. Then, the higher level hierarchical set can be obtained by the Kronecker product, i.e., (vI, kI, λI) ⊗ (vE, kE, λE), where operator ⊗ denotes the Kronecker product. The MACDS-based scheme used a multiplier set instead of the exponential set. All multiplier sets were selected from the relaxed difference sets (Luk and Wong 1997). The performance evaluation revealed that the MACDS-based scheme conserves more energy, whereas the EACDS is more suitable for scenarios where many different duty cycles are required.

Another extension of CDS-based protocol is to utilize permutation and combination to generate a longer cycle period for each sensor node (Su et al. 2016). Based on the Singer difference set, there exist \( q+1\, \left({C}_{q+1}^q\right) \) possible position combinations for q active slots in one subcycle. For example, when q = 2, we get a (7,3,1)–cyclic different set: D = {1,2,4}. There are three possible position combinations which has two active slots in one subcycle, and they are {1,2}, {1,4}, and {2,4}, as shown in Fig. 4.

One full operating cycle of a sensor node contains concatenation of all possible subcycles, each corresponding to a distinct position combination of any q active slots. Therefore, an operating cycle is comprised of (q + 1) subcycles, which corresponds to (q + 1) · (q2 + q + 1) slots, (q + 1) · q of which are active slots. Any ordering of subcycles can be used for concatenation. One working cycle period of a sensor node contains (q + 1) · (q2 + q + 1) active slots. Thus, the duty cycle is q/(q2 + q + 1). Figure 4 shows an example when q = 2.

Table 1 summarizes duty cycles used in Su et al. (2016) and the CDS-based protocol (Zheng et al. 2003). For a given value of q, the proposed scheme in Su et al. (2016) can generate a smaller duty cycle than the CDS-based protocol. It is obvious that a larger value of q can achieve a smaller duty cycle and thus reduce more energy consumption for idle listening. However, normally, for a given smaller duty cycle, a sensor node requires more active slots to connect to a next-hop sensor node.
Table 1

Duty cycles used in Su et al. (2016) and the CDS-based protocol (Zheng et al. 2003)

q

Duty cycles (%) (Su et al. 2016)

Duty cycles (%) (Zheng et al. 2003)

2

28.57

42.85

3

23.08

30.77

4

19.05

23.81

5

16.13

19.35

7

12.28

14.04

8

10.96

12.33

9

9.89

10.99

11

8.27

9.02

Tables 2 and 3 compare the proposed scheme in Su et al. (2016) that is extended using Kronecker product to the EACDS and MACDS proposed in Choi and Shen (2011), respectively. For EACDS, (vE, kE, λΕ) = (3,2,1). For MACDS, (vM, kM, λΜ) is selected from the rotational set group as used in Luk and Wong (1997). For the scheme proposed in Su et al. (2016) and the EACDS and MACDS, q = 2 is chosen as an example. From Tables 2 and 3, for given values of q, n, and (vM, kM, λΜ), the smaller duty cycles can be generated by the scheme proposed in Su et al. (2016) using the Kronecker product rather than EACDS and MACDS.
Table 2

Duty cycles used in Su et al. (2016) with Kronecker product and EACDS

n

Duty cycles (%) (Su et al. 2016)

Duty cycles in EACDS (%) (Choi and Shen 2011)

1

19.04

28.57

2

12.70

19.04

3

8.47

12.70

4

5.64

8.47

5

3.76

5.64

6

2.51

3.76

Table 3

Duty cycles used in Su et al. (2016) with Kronecker product and MACDS

(VM, kM, λΜ)

Duty cycles (%) (Su et al. 2016)

Duty cycles in MACDS (%) (Choi and Shen 2011)

(4,3,1)

21.43

32.14

(5,3,1)

17.14

25.71

(6,3,1)

14.29

21.42

(12,4,1)

9.52

14.29

(24,6,1)

7.14

10.71

(48,8,1)

4.76

7.14

Heterogenous CDS-Based Schemes

The length of a cycle period and the corresponding number of active slots determines the energy consumption of underwater sensor nodes when data exchanges do not frequently occur in long-term monitoring applications. When some nodes need to change the cycle length dynamically to satisfy the requirements of energy and packet delay and the other sensor nodes keep unchanged, the network connectivity will be lost when the wake-up coordination scheme do not guarantee that any two sensor nodes can communicate at least once in one cycle period. The heterogenous CDS-based schemes can keep the network connectivity when different CDS-based wake-up coordination schemes are adopted by different sensor nodes.

Cyclic quorum system pair (CQS-pair) is a heterogenous asynchronous wake-up coordination scheme based on the CDS-based protocol, which is derived by using the Singer cyclic difference set (Theorem 1) and the multiplier theorem (Theorem 2) (Lai et al. 2010). Moreover, a verification matrix is used to verify that any two heterogenous difference sets have the rotation closure property. To be more specific, sensor node a adopts a difference set {a1, a2, a3,…, ak} in (ZN, +) and sensor node b adopts another difference set {b1, b2, b3,…, bl} in (ZM, +), where N ≤ M and p = ⌈M/N⌉. The proposed verification matrix (Lai et al. 2010) is defined as a pk × l matrix Ml × pk whose element mi,j in the matrix is calculated by \( \left({b}_i-{a}_j^p\right) \) (mod M) and \( {a}_j^p\in {A}^p \), as presented as follows.
$$ {M}_{l\times pk}=\left[\begin{array}{ccc}{b}_1-{a}_1^p& \cdots & {b}_1-{a}_{pk}^p\\ {}\vdots & {b}_i-{a}_j^p& \vdots \\ {}{b}_l-{a}_1^p& \cdots & {b}_l-{a}_{pk}^p\end{array}\right] $$
(3)

Sensor node a and b have the rotation closure property if Ml × pk contains all elements from 0 to M – 1, which means these two heterogeneous difference sets can keep network connectivity when sensor nodes utilize them respectively.

For example, according to Theorems 1 and 2, there are two (7,3,1)–cyclic difference sets: {1,2,4} and {3,5,6} in (Z7, +) when a cycle period contains 7 slots. Similarly, there are two (21,5,1)–cyclic difference sets: {3,6,7,12,14} and {7,9,14,15,18} in (Z21, +) when a cycle period contains 21 slots. By using Eq. (3), sensor nodes can switch between a (7,3,1)–cyclic difference set with {1,2,4} and a (21,5,1)–cyclic difference set with {7,9,14,15,18} without sacrificing the network connectivity. Another pair of wake-up coordination schemes is a (7,3,1)–cyclic difference set with {3,5,6} and a (21,5,1)–cyclic difference set with {3,6,7,12,14}. Unlike the proposed schemes in Chao et al. (2006), Zheng et al. (2003), Choi and Shen (2011), and Su et al. (2016), the CQS-pair (Lai et al. 2010) provides more choices for sensor nodes to adapt dynamic changes of network requirements without further improving the energy consumption for long-term monitoring applications.

Key Applications

Asynchronous wake-up coordination scheme has been involved in different scenarios, such as long-term marine environmental monitoring, undersea resource explorations, assisted location and navigation, delay-tolerant data transmission, and some energy-limited applications. With the development of internet of underwater things (IoUT), the communications among multiple underwater autonomous vehicles (AUV) and the cooperation between AUV and glider can utilize asynchronous wake-up coordination scheme to prolong the network lifetime and extend the monitoring area.

Future Directions

Asynchronous wake-up coordination scheme brings energy efficiency for UWSNs. However, it also prolongs the data packet delay compared with on-demand and synchronous wake-up coordination schemes. Some challenges are presented below.
  • Balancing between energy consumption and packet delay

    It is obvious that the longer cycle period leads to low duty cycles for sensor nodes so as to significantly improve the energy efficiency. However, the packet delay is prolonged because not all sensor nodes are always active and the neighboring nodes discovery requires more time (or time slots) with a low duty cycle. Consequently, to achieve a balance between packet delay and energy consumption is a challenge for UWSNs when considering the nontrivial propagation delay by adopting asynchronous wake-up coordination scheme. On the other aspect, according to the data traffic or historic connection information (e.g., which active slots always enable the communication connection), how to select a proper duty cycle (i.e., network operation pattern such as (7,3,1)–cyclic difference set-s: {1,2,4}) achieves a balance between energy consumption and packet delay, which can be further investigated.

  • Mobility of sensor nodes

    In traditional UWSNs, all sensor nodes are supposed to be fixed on the seafloor in UWSNs. In the internet of underwater things (IoUT), AUVs serve as mobile sensor nodes, which can gather data in a large area for long-term monitoring applications. Directly utilizing aforementioned asynchronous wake-up coordination schemes may lead to nontrivial energy consumption and packet delay caused by the neighboring node discovery when a mobile sensor node is out of communication range of its intended nodes. How to adjust the asynchronous wake-up coordination scheme to accommodate the varying network topology in an energy-efficient manner is still an open issue in this area.

Cross-References

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61571241.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruoyu Su
    • 1
  • Dengyin Zhang
    • 1
    Email author
  • Ramachandran Venkatesan
    • 2
  • Cheng Li
    • 2
  • Zijun Gong
    • 2
  • Fan Jiang
    • 2
  1. 1.School of Internet of Things, Nanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Faculty of Engineering and Applied ScienceMemorial UniversitySt. John’sCanada

Section editors and affiliations

  • Cheng Li

There are no affiliations available