# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Motivation

#### 1.2. Problem Statement

#### 1.3. Contributions

- A hybrid superior node token ring MAC (HT-MAC) scheme is proposed, which is based on the virtual token ring of ordinary nodes, the polling of all superior nodes in one period, and alert transmissions with a low-power listening (LPL) and shortened preamble approach during the sleep state. A low-power clustering method for heterogeneous WSNs is proposed in order to increase the lifetime of disaster-monitoring systems;
- Based on embedded Markov chains, a model of the HT-MAC scheme was developed, and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained;
- Using the HT-MAC, WirelessHART, and DRX simulation models, simulations were performed under various conditions, and the delay specifications for the three types of data were explored. The theoretical results were verified through simulations. Based on the simulation results, this HT-MAC scheme satisfies the delay and throughput requirements for the three types of data in disaster-monitoring applications, and can enhance the effective lifetime of heterogeneous networks with the proposed clustering algorithm;
- An architecture for a collaborative disaster-monitoring system is proposed, and a method for obtaining seismic data using a highly energy-efficient approach is presented.

#### 1.4. Paper Organization

## 2. Related Work

#### 2.1. Contention-Based MAC

#### 2.2. Schedule-Based MAC

#### 2.3. Hybrid MAC

#### 2.4. Multiple Wireless Interfaces MAC

#### 2.5. Clustering Methods

## 3. Collaborative Disaster-Monitoring System

## 4. Scheme Description

#### 4.1. Set-Up Stage

_{rwtch}as the residual working time of the CH, as a cluster head, for the basis of cluster head selection:

_{r}is the residual energy of a sensor node, P

_{sc}is the mean power of sensing and calculation for this type of sensor node, and P

_{ar}is the mean power of data aggregation and relay as a cluster head for a sensor node. T

_{rwtch}indicates the persistent working time if a node is selected as a cluster head.

_{Tx}(l,d) is the energy of l-bit message transmission over distance d, E

_{elec}is the energy consumption for digital coding, modulation, filtering, and spreading of the signal, ε

_{fs}is the energy consumption of short distance transmission, and ε

_{mp}is the energy consumption of long distance transmission. In addition, d

_{0}is the threshold of the distance and can be obtained when d = d

_{0}, in Equation (2):

_{rwtch}, the residual working time, as a cluster head for each ordinary node. Subsequently, an ordinary node with the maximum T

_{rwtch}in a cluster is selected as the cluster head. Finally, the base station broadcasts information on the cluster heads and members of a cluster to all the sensor nodes. The information consists of the locations of the sensor nodes in a cluster; thus, the nodes can adjust their transmission power to fit the transmission distance within a cluster.

#### 4.2. Steady-State Stage

#### 4.3. Abnormity Handling

Algorithm 1. Abnormity handling when a node is unavailable. | |

Input: current node—current node with the polling tokenOutput: current node—current node with the new successor node | |

1 | A node delivers the polling token to its successor node; |

2 | if this node does not receive the token acknowledgement frame from the successor node |

3 | This node continues to deliver the polling token to the successor node several times; |

4 | if these retransmissions all fail |

5 | This node delivers the token for setting up a new successor to the next node of the successor node; |

6 | In the next node of the successor, the token is received and the predecessor node is set up with the current node; |

7 | The new successor node is set up with the next node of the successor; |

8 | The ring is reconfigured; |

9 | end if |

10 | end if |

11 | End |

Algorithm 2. Abnormity handling of multiple tokens. | |

Input: token maker—token with the ring address and token sequence numberOutput: current node—deleted token | |

1 | Token maker produces the ring address; |

2 | Token maker sets the token sequence number by adding one every time when it passes the token maker; |

3 | The nodes in the ring record the token identifier and sequence number of the polling token; |

4 | A node receives a new polling token; |

5 | if the sequence number is less than the previous one |

6 | Delete the received polling token; |

7 | else |

8 | if the token identifier is less than the previous token |

9 | Delete the polling token received; |

10 | else |

11 | This node transmits the data; |

12 | In this node, the polling token is transmitted to the successor node; |

13 | end if |

14 | end if |

15 | End |

## 5. Theoretical Analysis

_{i}. The service time for the frames transmitted by an ordinary node is independent. The distribution function of the service time in an ordinary node i is H

_{i}(x). P1 and P2 represent the queues of superior Nodes 1 and 2, respectively. The input of the superior nodes is a Poisson process, and in superior Nodes 1 and 2, the arrival rates of the Poisson process are λ

_{P}

_{1}and λ

_{P}

_{2}, respectively. The service time for the frames transmitted by a superior node is independent. The probability distribution of the frame transmission time is a general distribution in superior Nodes 1 and 2, and the distribution functions are H

_{P}

_{1}(x) and H

_{P}

_{2}(x), respectively. In the period from t

_{n}to t

_{n}

_{+1}, as shown in Figure 2, the data frame transmission time of ordinary node i is τ

_{i}, the sleep time is s

_{i}, and the polling token walking time is μ

_{i}. Within time t, the number of frames arriving in an ordinary node queue i is υ

_{i}(t). When the token ring system reaches a stable state, the instant at which ordinary node i begins to transmit frames, the probability of ordinary node k with j

_{k}frames waiting is g

_{i}(j

_{1}, j

_{2}, …, j

_{P}

_{1}, j

_{P}

_{2}). In ordinary nodes, assume that the instant when an ordinary node begins to transmit the data is …, t

_{n}, t

_{n}

_{+1}, …, so that … < t

_{n}< t

_{n}

_{+1}< …. According to the random process, random variables may be defined as follows: ε

_{n}(i) is the frame number in ordinary node I when the very moment is t

_{n}; ε

_{n}is the ordinary node identifier when ordinary node i starts transmitting packets in the period from t

_{n}to t

_{n}

_{+1}; in ordinary node i, h

_{i}is the mean frame transmission time; and h

_{P}

_{1}and h

_{P}

_{2}are the mean frame transmission times of superior nodes P1 and P2, respectively. Subsequently, the state of the system at t

_{n}is described by (ε

_{n}, ε

_{n}(1), ε

_{n}(2), …, ε

_{n}(N), ε

_{n}(P1), ε

_{n}(P2)); meanwhile, the state space of the system becomes I = {(i, k

_{1}, k

_{2}, …, k

_{j}, …,k

_{N}, k

_{P}

_{1}, k

_{P}

_{2}): i = 1, 2, …, N; k

_{j}= 0, 1, 2, …; j = 1, 2, …, N, P1, P2}. Hence, the transition probabilities with respect to the state (ε

_{n}, ε

_{n}(1), ε

_{n}(2), …, ε

_{n}(N), ε

_{n}(P1), ε

_{n}(P2)) construct an irreducible, aperiodic Markov chain. The Markov chain is ergodic, and the system can be assumed to be in statistical equilibrium. In this case, the limiting probabilities of the states (ε

_{n}, ε

_{n}(1), ε

_{n}(2), …, ε

_{n}(N), ε

_{n}(P1), ε

_{n}(P2)) are obtained as follows:

_{i}is the traffic intensity for ordinary node i, and ρ

_{P}

_{1}and ρ

_{P}

_{2}are the traffic intensities for superior nodes P1 and P2, respectively. The above equation shows that the system exists in an equilibrium state when the total traffic intensity of the system is less than 1.

_{P}

_{1}(x) and F

_{P2}(x) are the frame transmission times for superior nodes P1 and P2 at any instant t

_{i}with x frames, respectively. Between t

_{n}and t

_{n}

_{+1}, in superior nodes P1 and P2, the frame transmission times are F

_{P}

_{1}[ε

_{n}(P1)] and F

_{P}

_{2}[ε

_{n}(P2)], respectively.

_{n+}

_{1}:

_{i}, μ

_{i}and s

_{i}do not overlap:

_{n}

_{+1}(1), ε

_{n}

_{+1}(2), …, ε

_{n}

_{+1}(N), ε

_{n}

_{+1}(P1), ε

_{n}

_{+1}(P2)) is as follows:

_{n}

_{+1}(1), ε

_{n}

_{+1}(2), …, ε

_{n}

_{+1}(N), ε

_{n}

_{+1}(P1), ε

_{n}

_{+1}(P2)) is G

_{i}(x

_{1}, x

_{2}…, x

_{N}, x

_{P}

_{1}, x

_{P}

_{2}) as follows:

_{n}to t

_{n}

_{+1}, ${U}_{i}^{*}(s)$(i = 1,2, …, N) is the Laplace–Stieltjes transform of the walking time probability distribution; ${S}_{i}^{*}(s)$ is the Laplace–Stieltjes transform of the sleeping time probability distribution when the polling token moves forward from t

_{n}to t

_{n}

_{+1}. In ordinary node i with a Poisson input, H

_{i}(x) represents the busy period probability distribution, and ${H}_{i}^{*}(s)$ is the Laplace–Stieltjes transform of H

_{i}(x). In superior nodes P1 and P2 with a Poisson input, H

_{P}

_{1}(x) and H

_{P}

_{2}(x) represent the busy period probability distributions for P1 and P2, respectively, and ${H}_{P1}^{*}(s)$ and ${H}_{P2}^{*}(s)$ are the Laplace–Stieltjes transforms of H

_{P}

_{1}(x) and H

_{P}

_{2}(x), respectively. Using the above recursive formula, G

_{i}(x

_{1}, x

_{2}…, x

_{N}, x

_{P}

_{1}, x

_{P}

_{2}) can be described as a functional equation; however, an explicit representation cannot be derived.

_{i}(j) as the mean node queue length in queue j (j = 1, 2, …, N, P1, P2), when ordinary node i begins to transmit data, where i = 1, 2, …, N:

_{j}and x

_{l}→1 (l = 1, 2, …, N, P1, P2) yields

_{i}= λ, μ

_{i}= μ, h

_{i}= h, and s

_{i}= s, and the system is in the equilibrium state, the following can be set:

_{2}) can be expressed as

_{1}and P

_{2}, respectively. Thereafter, we can derive the mean cycle time of the token ring network when the ordinary nodes are symmetric and the system is in an equilibrium state:

## 6. Performance Evaluation

#### 6.1. Clustering Performance

_{rwtch}, the residual working time as a cluster head, is the variable that determines which node is the cluster head. We assume that a sensor node gains l bits of sensing data in time t

_{sc}; thus, P

_{sc}the mean power of sensing and calculation for this node is

_{sc}is the energy consumption for sensing and calculating this type of node. If a sensor node aggregates k bits of data and relays them to a base station at a distance d in time t

_{ar}, P

_{ar}is the mean power of the data aggregation and relay as a cluster head:

#### 6.2. Data Transmission Performance

## 7. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Situation when a node leaves a ring (1, 2, 3, and 4 denote nodes 1, 2, 3, and 4): (

**a**) when Node 1 is about to leave the ring; (

**b**) after Node 1 leaves.

**Figure 4.**Situation in which a node is unavailable (1, 2, 3, and 4 denote nodes 1, 2, 3, and 4): (

**a**) the normal ring; (

**b**) after abnormity handling.

**Figure 5.**Situation without a token in the ring (1, 2, 3, and 4 denote nodes 1, 2, 3, and 4): (

**a**) when Node 1 owns the polling token; (

**b**) after Node 1 departs from the coverage of all the other nodes.

**Figure 6.**Situation with multiple tokens (1, 2, 3, and 4 denote nodes 1, 2, 3, and 4): (

**a**) the normal situation; (

**b**) with multiple tokens.

Classification | Protocol |
---|---|

Contention-based MAC | S-MAC, B-MAC, PMAC, RT-MAC, MaxMAC, ENCO, QoS-MAC, DRX, PW-MAC, CSMA/WSD |

Schedule-based MAC | BMA, TRAMA, TDMA-W, ArDez, DMAC |

Hybrid MAC | CSMA-STDMA, E-hybrid, WirelessHART, Z-MAC, TCS |

Multiple wireless interfaces MAC | MMAC-HR, DSP |

Protocol | Main Scheme | Computation Overhead | Control Overhead | Adaptivity to Topology Changes | Delay | Time Synchronization Precision |
---|---|---|---|---|---|---|

S-MAC | Virtual cluster, adaptive listening | No | Rather high | Good | Rather high | Low |

B-MAC | LPL, clear channel assessment | No | High | Good | Moderate | Moderate |

PMAC | Pattern exchange, avoid overhearing | Pattern generation | High | Good | Moderate | Moderate |

RT-MAC | Feedback control, avoid contending | Clear channel control | Rather high | Low | Low | High |

MaxMAC | Extra wake-ups, traffic adaptation | Duty cycle control | High | Good | Moderate | Low |

ENCO | Estimated number of contenders | Contention window sizes | Moderate | Good | Moderate | Low |

QoS-MAC | Differentiated service | No | Rather low | Good | Moderate | Low |

DRX | Priority- and delay-aware | No | Rather low | Good | Low | Low |

PW-MAC | Predictive wakeup duty cycle | On-demand Prediction | Low | Good | Low | Moderate |

CSMA/WSD | Weak signal detection | Packet loss diagnosis | Low | Poor | Moderate | Low |

BMA | Clustered network, slot assignment | Slot assignment | High | Moderate | High | High |

TRAMA | Winning slot, reservation, piggy back | Transmission priority | High | Moderate | High | High |

TDMA-W | Graph-coloring, wakeup slot | Slot assignment | High | Moderate | High | High |

ArDez | Rendezvous-based scheme | Rendezvous period | High | Good | Moderate | Low |

DMAC | Staggered wakeup, data prediction | Data-gathering tree | Low | Rather poor | Rather poor | High |

CSMA-STDMA | CSMA initializing, STDMA data | Frequent slot assignment | Rather high | Poor | Moderate | High |

E-hybrid | Contention period, reserved slots | Slot assignment | Rather high | Poor | Low | High |

WirelessHART | Time-synchronized TDMA/CSMA | Slot assignment | High | Poor | Low | High |

Z-MAC | LPL, adaptability to contention level | Slot assignment | Low | Poor | Moderate | Moderate |

TCS | Token cycle scheduling | Slot scheduling | Moderate | Moderate | Low | High |

MMAC-HR | Multichannel, hopping reservation | Hopping reservation | High | Poor | Low | Low |

DSP | Multichannel, fast and slow hopping | Multiple rendezvous | High | Poor | Low | Low |

Description | Symbol | Value |
---|---|---|

Energy consumption of short distance transmission | ε_{fs} | 10 pJ/bit/m^{2} |

Energy consumption of long distance transmission | ε_{mp} | 0.0013 pJ/bit/m^{4} |

Energy consumption for digital coding, modulation, filtering, and spreading of the signal | E_{elec} | 50 nJ/bit |

Energy for data aggregation | E_{DA} | 5 nJ/bit/signal |

Energy for sound-sensing and calculating | E_{sc-sou} | 50 pJ/bit |

Energy for picture-sensing and calculating | E_{sc-pic} | 5 nJ/bit |

Energy for seismicity-sensing and calculating | E_{sc-sei} | 3 nJ/bit |

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## Share and Cite

**MDPI and ACS Style**

Chen, D.; Zhang, Y.; Pang, G.; Gao, F.; Duan, L.
A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks. *Sensors* **2023**, *23*, 5068.
https://doi.org/10.3390/s23115068

**AMA Style**

Chen D, Zhang Y, Pang G, Gao F, Duan L.
A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks. *Sensors*. 2023; 23(11):5068.
https://doi.org/10.3390/s23115068

**Chicago/Turabian Style**

Chen, Danqi, Yanxia Zhang, Guoli Pang, Fangping Gao, and Li Duan.
2023. "A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks" *Sensors* 23, no. 11: 5068.
https://doi.org/10.3390/s23115068