**8. Conclusion**

*Wireless Sensor Networks - Design, Deployment and Applications*

**258**

**Predictive** 

**Examples**

**Classification of** 

**Characteristics**

**Advantages**

**Disadvantages**

Assumes univariate data

Data gathering

applications

Water Quality

monitoring

Medical data

monitoring

Agricultural

monitoring

Environmental

monitoring

Multimedia

VANETS

analysis

**Applications in** 

**WSN**

**data**

**Model**

ARIMA models

Naïve

Naïve bayes

Trend

Distributed, partially

Independence of input data if trained.

Assumes independent

predictor features.

Useful in applications with frequent

topology changes if model is well

trained.

More significant to recent

Ignores random

variations of data.

May not handle trends

well

observations.

Best for short-term forecasts

independent

algorithms

Exponential

Single Exponential

Trends and

Univariate

seasonal data

smoothing

Double

Exponential

smoothing

Triple exponential

smoothing

Gray Series

GM (1,1)

Trend, seasonal,

Univariate and

Requires limited samples for short

Homogenous exponent

Energy-map

applications

Water quality

monitoring

Environmental

monitoring

Internet of Things

Target tracking

Temperature

measurements.

simulative deviation

term prediction.

Can make near accurate prediction

with poor information of the data.

multivariate data.

Rolling GM (1,1)

Adaptive Gray

Least Mean

LMS

Trend

Univariate

Distributed algorithm

Not robust frequent

change is trends

Does not require a-priori knowledge

of the environment

Hierarchical LMS

Square

**Table 4.**

*Time series classification.*

Smoothing

AR, MA, ARMA,

Seasonal, Trend

Univariate, linear and

Independence from external data.

Does not require extended analysis

non-linear, discrete

ARIMA

The chapter focuses on the various deployment environments and the challenges that the implementation of WSNs brings about when adopted for use. The chapter also discussed WSN topologies and explored ways in which network topology impacts energy consumption and communication issues. The chapter highlights the different techniques mainly used when implementing WSNs to maximize lifetime, coverage, and connectivity while ensuring data fidelity. The chapter also describes the energy conservation techniques, data prediction approaches, and mobility models. Various data prediction models such as the time series models are discussed in detail. A classification of some time series models based on their popularity and recent use highlights their applications. Advantages and disadvantages of the selected time series models and reasons of their implementations are discussed.
