Conference PaperPDF Available

Energy-Efficient Particle Swarm Optimization for Lifetime Coverage Prolongation in Wireless Sensor Networks

August 2020

August 2020

DOI:10.1007/978-3-030-55340-1_15

Conference: NTICT 2020

Authors:

Ali Kadhum Idrees

University of Babylon

Safaa Almamory

University of Information technology and Communications

Raphaël Couturier

Université de Franche-Comté

Preserving sufficient coverage and prolonging the network lifetime as much as possible has become one of the most critical issues in Wireless Sensor Networks (WSNs). In this article, a protocol called Energy-efficient Particle Swarm Optimization for Lifetime Coverage Prolongation (EPSOLCOP) is proposed to maintain the coverage and enhance the WSN lifetime. The target sensing field is virtually partitioned into smaller subfields. EPSOLCOP protocol is distributed on the sensor nodes of each subfield in the WSN. The lifetime of each subfield is divided into discovery and activity rounds of the same length. After a neighbor discovery, each round consists of three phases: cluster head election, sensor activity scheduling-based particle Swarm Optimization (PSO), and monitoring phase. The cluster head executes the PSO so as to produce the best representative set of sensor nodes which are responsible for covering the subfield in the next phase. Each set is produced to ensure the coverage at a low energy cost, allowing to optimize the WSN lifetime. In comparison with some existing protocols, simulation results done using the discrete event simulator OMNeT++ show that EPSOLCOP protocol is very competitive by achieving a high coverage ratio with reduced energy consumption.

EPSOLCOP protocol.

…

Coverage ratio for 200 deployed nodes.

…

Active sensors ratio for 200 deployed nodes.

…

Figures - uploaded by Ali Kadhum Idrees

Content may be subject to copyright.

Content uploaded by Ali Kadhum Idrees

Content may be subject to copyright.

1,∗[0000−0001−9773−0066]

2[0000−0003−3144−8222] 3 [0000−0003−1490−9592]

∗

· ·

d3

P size

P rth

XP r = (XP r

1, XP r

2, ..., XP r

d, ..., XP size

f(X)P rth

VP r = (VP r

1, V P r

2, ..., V P r

d, ..., V P siz e

LbP r = (LbP r

1, LbP r

2, ..., LbP r

d, ..., LbP r

Gb = (Gb1, Gb2, ..., Gbd, ..., GbD)

MAXG

VP r

d(t+ 1) = ω×VP r

d(t) + c1×r1×(LbP r

d(t)−XP r

d(t))

+c2×r2×(Gbd(t)−XP r

d(t))

XP r

d(t+ 1) = XP r

d(t) + VP r

d(t+ 1)

c1c2r1r2

{0,1}

XP r

d(t+ 1) = 0r≥Sig(VP r

d(t+ 1))

Sig(VP r

d(t+ 1))

Sig(VP r

d(t+ 1)) = 1

1 + e−VP r

d(t+1)

P r

LbP r (t+ 1) = LbP r (t)f(XP r (t+ 1)) ≥f(LbP r (t))

XP r (t+ 1) f(XP r (t+ 1)) < f(LbP r (t))

GbP r (t+ 1) = 









arg max

LbP r (t+1)

f(LbP r (t+ 1))

max f(LbP r (t+ 1)) ≥f(GbP r (t))

GbP r (t)

max f(LbP r (t+ 1)) < f(GbP r (t))

Rc≥2Rs

S={s1, . . . , sN}

A={A1, . . . , Ak, . . . , ANR}NR

δk={s1, ..., sj, ..., sNk}

siEi

REiEisi

ρk={P1, . . . , Pp, . . . , PNk}Nk

Dj=1sj

Γjp =





1Pp

Γjp

P SE T kAk

Minimize

p=1

j=1

Γjp ∗Dj

p=1

j=1

Γjp ∗Dj=Nk

Dj∈ {0,1},∀sj∈Ak

SS ET k

AkA

sjAk

P r X Pr

jVP r

LbP r

jGbj16P r 6P siz e 16j6Nk

P r {X1, . . . , Xj, . . . , XNk}

X sjAk

XP r

j=1rnd > 0.5

∈[0,1]

V P r

VP r

j=V min + (V max −V min)∗rnd

[V min, V max]

VP r

j=





V max V P r

j> V max

V min V P r

j< V min

VP r

ω , c1c2

ω= (start −end)∗((M AXG −t)/MAX G) + end

c1= (end −start)∗((M AXG −t)/MAX G) + start

c2= (start −end)∗((M AXG −t)/MAX G) + end

Gfit LfitP r

FP r

FP r =

p=1

j=1

Γjp ∗Dj

XP r

j=





1rnd < XP r

j+VP r

j+V max

1+2∗V max 

(MAXG∗P siz e∗Nk∗Nk)

(MAXG)

Θ(MAXG ∗P size ∗Nk)

Θ(P size ∗Nk∗Nk)

Γk

P r ←1P size

XP r 0 1

VP r

f(XP r )

LbP r ←XP r

f(LbP r )← −∞

f(Gb)← −∞

t6MAXG

P r ←1P size

f(XP r )> f (LbP r )

LbP r ←XP r

f(LbP r )> f (Gb)

Gb ←LbP r

ω , c1c2

P r ←1P size

VP r

XP r

t←t+ 1

(50 ×25) m2

Ethr

MAXG 1000

P size 30

(50×25) m2

[500 −700]

Eth = 36Joules

Eth

35330

2×1

6

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Coverage Ratio (%)

Number of Rounds

DESK

GAF

EPSOLCOP

Lifetime95 Lifetime50

0 10 20 30 40 50 60 70 80 90 100 110 120

Active Sensor Ratio (%)

Number of Rounds

DESK

GAF

EPSOLCOP

Lifetime95 Lifetime50

1000

1250

1500

1750

2000

2250

2500

2750

3000

3250

3500

75 100 125 150 175 200 225 250 275 300 325

Energy Consumption Per Round (Joules)

Number of Wireless Sensor Nodes

DESK

GAF

EPSOLCOP

1000

1250

1500

1750

2000

2250

2500

2750

3000

3250

75 100 125 150 175 200 225 250 275 300 325

Energy Consumption Per Round (Joules)

Number of Wireless Sensor Nodes

DESK

GAF

EPSOLCOP

Lifetime95 Lifetime50

75 100 125 150 175 200 225 250 275 300 325

Network Lifetime (Hours)

Number of Wireless Sensor Nodes

DESK

GAF

EPSOLCOP

100

110

120

130

140

150

75 100 125 150 175 200 225 250 275 300 325

Network Lifetime (Hours)

Number of Wireless Sensor Nodes

DESK

GAF

EPSOLCOP

Lifetime95 Lifetime50

A novel energy-aware method for clustering and routing in IoT based on whale optimization algorithm & Harris Hawks optimization

Article

Full-text available

Feb 2024
COMPUTING

Ehsan Heidari

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

Data reduction techniques for wireless multimedia sensor networks: a systematic literature review

Article

Full-text available

Dec 2023
J SUPERCOMPUT

The potential of Internet of Things and wireless sensor networks technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. Wireless multimedia sensor network represents a distinct subdomain within the broader scope of wireless sensor networks. It has found diverse applications in the context of future smart cities, particularly in areas such as healthcare monitoring, home automation, transportation systems, and vehicular networks. A wireless multimedia sensor network is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by wireless multimedia sensor network across the Internet of Things network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and wireless multimedia sensor network lifespan. One of the solutions to these kinds of problems is data transmission reduction. Therefore, this systematic literature review will review various techniques used in data transmission reduction, ranging from data transmission redundancy reduction to machine learning algorithms. Additionally, this review investigates the range of applications and the challenges encountered within the domain of wireless multimedia sensor networks. This work can serve as a basic strategy and a road map for scholars interested in data reduction techniques for intelligent surveillance systems using WMSN in Internet of Things networks.

Maximizing The Lifetime of WSN Using Hybrid Energy Harvesting Approach

Conference Paper

May 2022

The 21st century is marked by a remarkable expansion of IoT. This technology that allows us to connect with our objects permanently and thus monitor each interaction that may be crucial in our daily life. And among the most important bronchi of this technology we are interested in wireless sensor networks with mobile sink (WSN-MS1 for short). These networks, while being very useful and efficient in the world of internet of things (IoT)[33,34], they face serious challenges, particularly that of the battery which ensures them a very limited lifetime, as well as the problem of the broken links between the transmitting nodes and the mobile sink. In this paper, we have proposed a new approach EH-HXMAC that solves the first problem by collecting ambient energy from the two most powerful sources (the sun and the wind), and by acting on the duty cycle of the emitting nodes. To remedy the second problem, we have resorted to the Handover technique which ensures data transmission integrity between the sensor and the mobile sink. The results of the simulation carried out by the Cooja Contiki simulator show that the combination of these three techniques provides an improvement in terms of network lifetime , throughput and received packets number. Keywords— Wireless Sensor Networks with mobile sink, HXMAC, Energy Harvesting, Duty cycling, Handover, Simulation and performance evaluation.

Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm

Article

Full-text available

Jul 2022

Soil moisture wireless sensor networks (SMWSNs) are used in the field of information monitoring for precision farm irrigation, which monitors the soil moisture content and changes during crop growth and development through sensor nodes at the end. The control terminal adjusts the irrigation water volume according to the transmitted information, which is significant for increasing the crop yield. One of the main challenges of SMWSNs in practical applications is to maximize the coverage area under certain conditions of monitoring area and to minimize the number of nodes used. Therefore, a new adaptive Cauchy variant butterfly optimization algorithm (ACBOA) has been designed to effectively improve the network coverage. More importantly, new Cauchy variants and adaptive factors for improving the global and local search ability of ACBOA, respectively, are designed. In addition, a new coverage optimization model for SMWSNs that integrates node coverage and network quality of service is developed. Subsequently, the proposed algorithm is compared with other swarm intelligence algorithms, namely, butterfly optimization algorithm (BOA), artificial bee colony algorithm (ABC), fruit fly optimization algorithm (FOA), and particle swarm optimization algorithm (PSO), under the conditions of a certain initial population size and number of iterations for the fairness and objectivity of simulation experiments. The simulation results show that the coverage rate of SMWSNs after ACBOA optimization increases by 9.09%, 13.78%, 2.57%, and 11.11% over BOA, ABC, FOA, and PSO optimization, respectively.

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Book

Jun 2021

Energy-efficient Scheduling based Clustering Approach for Wireless Sensor Networks of IoT

Conference Paper

Dec 2022

Several Internet of Things (IoT) applications employ Wireless Sensor Networks (WSNs) in monitoring such as health-care, environment, transportation, agriculture, etc. These applications will produce a significant amount of data, which will pose a big challenge in the Big Data age. Scheduling is one of the energy-efficient mechanisms that can be used to deal with such a large volume of data. In this paper, an Energy-efficient Scheduling based Clustering Approach (ESCA) for Wireless Sensor Networks (WSNs) of IoT is proposed. ESCA works into rounds. each round composed of two periods. The first period consists of three steps: data collection, compressed transmission, and scheduling-based clustering. The scheduling algorithm plans the schedule for the WSN in the first period of each round for all the periods in the round. The second period consists of collection and compressed transmission. The active sensor nodes will only collect and transmit compressed data to the sink in the second period of each round. The simulation results based on real sensed data of the sensor network show that the proposed ESCA technique can reduce transmitted data up to 78.65% and decrease the energy consumption up to 98.13% in comparison with another method.

Shuffled ARSH-FATI: A Novel Meta-Heuristic for Lifetime Maximization of Range-Adjustable Wireless Sensor Networks

Article

Sep 2023

Wireless Sensor Network (WSN) is an essential component of the Internet-of-Things (IoT), enabling the creation of a vast network of interconnected devices and systems that can interact with each other and with the physical world. WSNs are widely used for a variety of applications, including environmental monitoring, healthcare, industry 4.0, agriculture, transportation, and surveillance. WSN consists of devices with limited residual energy therefore, the lifetime improvement of WSN is critical for achieving the full potential of IoT and enabling the creation of more robust and reliable connected systems. This paper explores the problem of Lifetime Maximization of Range Adjustable Sensors (LM-RAS). The goal is to optimize the lifetime of the WSN consisting of range-adjustable sensors while satisfying two constraints. Firstly, all targets in the network must be monitored simultaneously. Secondly, the activation time of each sensor should be limited by its battery energy. A novel meta-heuristic called Shuffled ARSH-FATI has been proposed that embeds ARSH-FATI into a multi-population method. Shuffled ARSH-FATI solves the LM-RAS problem by splitting it into two sub-problem. The first sub-problem deals with the creation of energy-efficient coverage schemes and the second sub-problem deal with the schedule of these schemes. Shuffled ARSH-FATI utilizes a Linear Programming (LP) model to generate an optimal schedule for the coverage schemes but its performance greatly depends on the quality of the coverage schemes. Shuffled ARSH-FATI simultaneously explores several regions of the search space by partitioning the coverage schemes in population into several sub-populations. It evolves each sub-population (in this problem a sub-set of coverage schemes) independently using ARSH-FATI. It incorporates the shuffling process via Merge, Shuffle, and Partition (MSP) algorithm that merges the sub-populations, shuffles, and then partitions the merged population into new sub-populations. The shuffling process along with the strategy to fine-tune the coverage scheme into energy-efficient coverage schemes maintains population diversity throughout the optimization. The diverse high-quality coverage scheme improves the performance of the LP model.

Energy-efficient Data Processing Protocol in Edge-based IoT Networks

Article

Mar 2023
ANN TELECOMMUN

Wireless sensor networks (WSNs) represent an essential element of many applications in the Internet of Things (IoT) network and smart cities in the present and future. The sensor devices in these WSNs gather a lot of data, which will be sent to the edge gateway periodically. This would deplete the devices' limited battery power and degrade the network's performance. Therefore, it is important to turn off the redundant sensors that transmit the same data to the gateway and activate the minimum number of sensor nodes in the IoT network. This reduces the redundant sensed readings and decreases the overhead of communications, thereby extending the WSN's lifetime. In this paper, an energy-efficient data processing (EDaP) protocol for edge-based IoT networks is proposed. The proposed EDaP protocol is implemented at two levels: sensor devices and the edge gateway. The data is collected by sensor devices and then encoded using either Huffman Encoding (HE) or the proposed Modified Run Length Encoding (MRLE). At the edge gateway level, the sensor node scheduling algorithm is implemented by the EDaP protocol to produce the best sensor schedule to fulfill the monitoring mission in the next period. The sensor nodes are scheduled based on the spatial correlation between their collected data using clustering methods. The simulation results are conducted to prove the effectiveness of the proposed technique, where it provides competitive results in comparison with some other work in terms of energy consumption, active sensor ratio, transmitted data ratio, and percentage of lost data.

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Conference Paper

Full-text available

Jun 2021

The network topology control represents an essential factor during designing the Wireless Sensor Networks (WSNs) due to its primary role in the lifetime optimization of the WSNs. This paper proposes a Cuckoo Scheduling Algorithm (CSA) for lifetime optimization in Sensor Networks of Internet of Things (IoT). First, the sensor devices have clustered using the DBSCAN method, and then the CAS technique is applied at each cluster head. The CSA provides the best schedule of the sensor nodes inside each cluster to monitor the cluster region of with a minimum number of nodes while keeping a suitable level of coverage. The cluster head pooling and the CSA have executed periodically. The experimental results confirm that the CSA technique enhances the network lifespan and whilst preserving an acceptable ratio of coverage in WSNs.

Two Level Data Aggregation Protocol for Prolonging Lifetime of Periodic Sensor Networks

Article

Full-text available

Aug 2019
WIREL NETW

One big contributor in the future of the Internet of Things (IoT) is the Periodic Sensor Networks (PSNs) because it has been used by many applications in real life. The main challenge in this type of networks is to gather the huge amount of data periodically in an energy saving way and then transmit them to the base station in order to extend the lifetime of PSN. Since the limited nature of the sensors batteries power, therefore, an energy-efficient data aggregation method is needed to optimize both energy and lifetime in PSNs. This article proposes a Two Level Data Aggregation (TLDA) Protocol for Pro- longing the Lifetime of Periodic Sensor Networks. TLDA works in a periodic way. Each period consists of two data aggregation levels. The first level of data aggregation is applied at the sensor node. This level includes data collection, the sliding window to generate a varying number of segments with different lengths, and data aggregation using Adaptive Piecewise Constant Approximation (APCA) technique to reduce the amount of data collected by each sensor. The second level is applied at the aggregator. It includes grouping received data sets based on the chaining hash table with SAX quantization method, finding and lowering the duplicate sets, finding and merging the duplicate readings, and transmit the aggregated data to the sink. Extensive simulation results are conducted using OMNeT++ network simulator and based on real data of sensor network to show the efficiency of the TLDA protocol compared with two existing methods.

Data Transmission Protocol for Reducing the Energy Consumption in Wireless Sensor Networks

Conference Paper

Full-text available

Sep 2018

One of the crucial factors in designing Wireless Sensor Networks (WSNs) is energy management. In order to save the limited energy of the WSNs, it is essential to reduce the redundant data during the aggregation inside the sen-sor node. This can lead to eliminating the communication cost related to sending unnecessary data. Therefore, it is necessary to implement a data re-duction method inside each sensor node to remove non-useful data before transmitting it to the sink. This paper proposed Data Transmission (DaT) Pro-tocol for Reducing the data transmission cost inside each sensor node by get-ting rid of the redundant data to save the energy while maintaining a suitable level of accuracy in the received readings at the sink. DaT protocol partitions the WSN lifetime into periods. Two stages are found in every period. The first stage is the data classification in which the collected sensed data are classified into several classes based on the Modified k-Nearest Neighbor technique. The similar classes are merged into one class to further reduce the data size transmitted to the sink. In the second stage, the best representative reading from every class is selected to send it to the sink. DaT protocol is evaluated using real recorded data of the temperature and using OMNeT++ network simulator to show the performance of the proposed protocol. Com-pared with other recent methods, DaT protocol can reduce the energy con-sumption, the data loss ratio, and the size of the data after applying data re-duction.

Data Transmission Protocol for Reducing the Energy Consumption in Wireless Sensor Networks

Conference Paper

Full-text available

Jul 2018

One of the crucial factors in designing Wireless Sensor Networks (WSNs) is en-ergy management. In order to save the limited energy of the WSNs, it is essential to reduce the redundant data during the aggregation inside the sensor node. This can lead to eliminating the communication cost related to sending unnecessary da-ta. Therefore, it is necessary to implement a data reduction method inside each sensor node to remove non-useful data before transmitting it to the sink. This pa-per proposed Data Transmission (DaT) Protocol for Reducing the data transmis-sion cost inside each sensor node by getting rid of the redundant data to save the energy while maintaining a suitable level of accuracy in the received readings at the sink. DaT protocol partitions the WSN lifetime into periods. Two stages are found in every period. The first stage is the data classification in which the col-lected sensed data are classified into several classes based on the Modified k-Nearest Neighbor technique. The similar classes are merged into one class to fur-ther reduce the data size transmitted to the sink. In the second stage, the best rep-resentative reading from every class is selected to send it to the sink. DaT proto-col is evaluated using real recorded data of the temperature and using OMNeT++ network simulator to show the performance of the proposed protocol. Compared with other recent methods, DaT protocol can reduce the energy consumption, the data loss ratio, and the size of the data after applying data reduction. Keywords: Wireless Sensor Networks (WSNs); Periodic Applications; Data Reduction; Modified k-Nearest Neighbour; Energy efficiency.

Distributed Data Aggregation based Modified K-means Technique for Energy Conservation in Periodic Wireless Sensor Networks

Conference Paper

Full-text available

Mar 2018

One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wireless Sensor Networks (PWSNs) because of the widespread use of this type of networks in various real-life applications. The amount of data clearly grows at an unexpected rate. The high-density deployment of the sensor nodes will lead to high data redundancy in the collected readings of the sensor nodes. An energy-saving data aggregation may be an essential way to remove the data redundancy. In this article, we propose a Distributed Data Aggregation based Modified K-means (DiDAMoK) Technique for enhancement the lifetime of the PWSNs. DiDAMoK is distributed inside each sensor node. It works into periods. Each period composed of three stages. First, the sensor readings are collected and saved in the sensor node. Second, the modified K-means is employed on these readings to convert them into clusters of readings. The number of clusters is dynamic and depends on the nature of collected readings. In the third stage, One representative reading of each cluster will be transmitted to the sink. The performance of the DiDAMoK technique is evaluated using OMNeT++ network simulator and based on real sensed data of a WSN. Simulation results explain that our DiDAMoK technique can efficiently decrease the consumed energy of the whole PWSN due to reducing the sensed readings number transmitted to the sink node while keeping a suitable data accuracy at the sink.

Distributed Genetic Algorithm for Lifetime Coverage Optimization in Wireless Sensor Networks

Article

Full-text available

Jan 2021

The coverage problem represents a research challenge in designing energy-efficient Wireless Sensor Networks (WSNs), in which both coverage ratio and energy saving should be considered. In this paper, a protocol called Distributed Genetic Algorithm for Lifetime Coverage Optimization (DiGALCO) is suggested to preserve the coverage and enhance the lifetime of a WSN. DiGALCO protocol is based on two steps. First, the sensing field is logically divided into smaller uniform subfields. Second, DiGALCO protocol is then implemented at each sensor node in each subfield. To achieve our goal, the proposed protocol combines three energy-efficient schemes: virtual network subdivision into subfields, distributed cluster head selection in each subfield, followed by sensor activity scheduling based Genetic Algorithm (GA) optimization performed by each elected cluster head. DiGALCO protocol works into rounds. More precisely, a round consists of three phases: (i)~Discovery, (ii)~Cluster head selection, (iii)~GA Decision and Sensing. The decision process, which results in an activity scheduling vector, is carried out by a cluster head node through executing the GA in order to pick out a set of sensors staying active for monitoring through the current sensing round. Every set is constructed to guarantee coverage at a low cost of energy, enabling to improve the WSN lifetime. In comparison with some other protocols, several experimental results were done by using OMNeT++ network simulator show that DiGALCO protocol is capable of prolonging the lifetime of WSN and gives enhanced coverage performance.

Multiround Distributed Lifetime Coverage Optimization protocol in wireless sensor networks

Article

Full-text available

May 2018
J SUPERCOMPUT

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance.

Distributed Adaptive Data Collection Protocol for Improving Lifetime in Periodic Sensor Networks

Article

Full-text available

Aug 2017

Periodic Sensor Networks (PSNs) represent one of the essential elements of emerging Cyber-Physical System (CPS) designs because of their using in multiple applications. PSNs are one of the major contributors of the big data in the future. One fundamental challenge in PSNs is to periodically collect the large volume of data in an energy efficient way and then transmit them to the sink so as to enhance the network lifetime. Since sensor batteries have a limited lifetime, therefore, adaptive sampling method to periodic data collection is required to support energy-efficient data gathering and fusion of CPS. In this research, we suggest a protocol, called Distributed Adaptive DAta Collection protocol (DADAC), which collects periodically sensor readings and prolong the lifetime of a Periodic Sensor Network (PSN). The lifetime of DADAC protocol is divided into cycles. Each cycle is composed of four stages. First, data collection. Second, dimensionality reduction using Piecewise Aggregate Approximation (PAA) technique to reduce the amount of data collected by each sensor. Third, Frequency Reduction using SAX (Symbolic Aggregate approXimation) approach in order to remove the redundant data before send them to sink. Fourth, sampling rate adaptation based PAA similarity to acclimate its rate of sampling according to the dynamic modification of the monitored environment. DADAC allows each sensor to remove the redundant collected data and adapts its sampling rate in accordance with the monitored environment conditions. We conduct extensive simulation experiments on real sensor data by applying OMNeT++ network simulator to explain the effectiveness of the DADAC protocol in comparison with two other existing methods.

Energy-efficient adaptive distributed data collection method for periodic sensor networks

Article

Jan 2018

This article suggests a method, called energy-efficient adaptive distributed data collection method (EADiDaC), which collects periodically sensor readings and prolong the lifetime of a periodic sensor network (PSN). The lifetime of EADiDaC method is divided into cycles. Each cycle is composed of four stages. First, data collection. Second, dimensionality reduction using adaptive piecewise constant approximation (APCA) technique. Third, frequency reduction using symbolic aggregate approximation (SAX) approach. Fourth, sampling rate adaptation based dynamic time warping (DTW) similarity. EADiDaC allows each sensor to remove the redundant collected data and adapts its sampling rate in accordance with the monitored environment conditions. The simulation experiments on real sensor data by applying OMNeT++ simulator explains the effectiveness of the EADiDaC method in comparison with two other existing methods.

Wireless Sensor Networks: A Big Data Source in Internet of Things

Article

Sep 2017

Devices connected to the internet are increasing day a day, the era of Internet of Things (IoT) is coming. However, handling big data generated by the IoT networks will present considerable challenge for the decision makers. Wireless sensor networks (WSNs) is one of the big data source in IoT. In such networks, a wide range of areas are monitored by thousands of sensors where gathered data are sent to the sink node. Unfortunately, WSNs impose many challenges compared to other types of networks. Data management is a mainly challenging task for WSNs due to the huge amounts of data collected in such networks. To the best of our knowledge, this paper represents the first formal overview about big data management in WSNs. We highlight and categorize, based on some performance measures, different algorithms designed for data collection, aggregation, correlation, compression and prediction in such networks. Furthermore, we give a review about most important WSN applications. Finally, open research issues of WSNs are also suggested and put forward in this paper.

Coverage Contribution Area Based $k$-Coverage for Wireless Sensor Networks

Article

Mar 2017

Coverage is a primary metric for ensuring the quality of services (QoSs) provided by a wireless sensor network (WSN). In this paper, we focus on the k-coverage problem, which requires a selection of a minimum subset of nodes among the deployed ones such that each point in the target region is covered by at least k nodes. We present both centralized and distributed protocols to tackle this fundamental problem. Our protocols are based on a novel concept of Coverage Contribution Area (CCA), which helps to get a lower sensor spatial density. Furthermore, our protocols take the residual energies of the sensors into consideration. This consideration combining with the low sensor spatial density ensures that our protocols can prolong the network lifetime to a greater extent, which is crucial to WSNs due to the limited energy supply and the difficulties for energy recharging. We also conduct extensive simulations to verify our proposed algorithms, and the results show that they are superior over existing ones.

Energy-Efficient Particle Swarm Optimization for Lifetime Coverage Prolongation in Wireless Sensor Networks

Abstract and Figures

Recommended publications

Distributed lifetime coverage optimization protocol in wireless sensor networks

Energy-saving Distributed Monitoring-based Firefly Algorithm in Wireless Sensor Networks

ETOP: Energy-Efficient Transmission Optimization Protocol in Sensor Networks of IoT

Energy-efficient Two-layer Data Transmission Reduction Protocol in Periodic Sensor Networks of IoTs

Distributed Genetic Algorithm for Lifetime Coverage Optimization in Wireless Sensor Networks