Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks

Ari, Ado Adamou Abba; Damakoa, Irépran; Guéroui, Abdelhak Mourad; Titouna, Chafiq; Labraoui, Nabila; Kaladzavi, Guidedi; Yenké, Blaise Omer

doi:10.1007/s10776-017-0359-y

Cited by 28 publications

(16 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most critical issue in mobile WSNs is the mobile aggregation scheduling problem. Reference [43] proposed a BFO-based cooperative mobile sensing scheme in mobile WSNs using UAV as a mobile sink. The process of Escherichia coli moving in BFO was used to simulate the distributed self-organization and adaptive displacement of the mobile sink.…”

Section: Si Applications In Uav-aided Wsnsmentioning

confidence: 99%

A Survey of Using Swarm Intelligence Algorithms in IoT

Sun

Tang

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

show abstract

Section: Si Applications In Uav-aided Wsnsmentioning

confidence: 99%

A Survey of Using Swarm Intelligence Algorithms in IoT

Sun

Tang

Zhang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, a poor deployment strategy can lead to unbalanced energy depletion, resulting in empty areas over time while others remain quite dense. Therewith, in the literature, the WSN deployment techniques proposed so far have in most cases been designed to either maximize network connectivity, increase coverage of the area of interest or, of course, extend the network's lifespan [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

Self Cite

View full text Add to dashboard Cite

Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

show abstract

“…For example, the literature [10] proposes an improved artificial bee colony algorithm to solve the traveling salesman problem of neighborhood search in sparse WSNs and obtains the optimal data collection and movement paths of mobile sensor nodes. The literature [11] proposes autonomous movement model of mobile sensor node, which can be solved by bacterial foraging optimization algorithm (BFOA). The autonomous mobile solution of each mobile sensor node can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…The autonomous mobile solution of each mobile sensor node can be obtained. However, the literatures [8][9][10][11] do not consider that the storage space of mobile sensor nodes is limited and have large packet loss rate and high data transmission delay.…”

Section: Introductionmentioning

confidence: 99%

Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

Zhao

Chen

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

show abstract

Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks

Cited by 28 publications

References 28 publications

A Survey of Using Swarm Intelligence Algorithms in IoT

A Survey of Using Swarm Intelligence Algorithms in IoT

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

Contact Info

Product

Resources

About