Machine learning algorithms for wireless sensor networks: A survey

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Energy hole problem is a critical issue for data gathering in wireless sensor networks. Sensors near the static sink act as relays for far sensors and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to alleviate this problem. However, determining an optimal moving trajectory for a mobile sink is a non-deterministic polynomial-time hard problem. Thus, this paper proposed a mobile sink-based adaptive immune energy-efficient clustering protocol (MSIEEP) to alleviate the energy holes. A MSIEEP uses the adaptive immune algorithm (AIA) to guide the mobile sink-based on minimizing the total dissipated energy in communication and overhead control packets. Moreover, AIA is used to find the optimum number of cluster heads (CHs) to improve the lifetime and stability period of the network. The performance of MSIEEP is compared with the previously published protocols; namely, low-energy adaptive clustering hierarchy (LEACH), genetic algorithm-based LEACH, amend LEACH, rendezvous, and mobile sink improved energy-efficient PEGASIS-based routing protocol using MATLAB. Simulation results show that MSIEEP is more reliable and energy efficient as compared with other protocols. Furthermore, it improves the lifetime, the stability, and the instability periods over the previous protocols, because it always selects CHs from high-energy nodes. Moreover, the mobile sink increases the ability of the proposed protocol to deliver packets to the destination.

Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks

In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay In this paper, several clustering methods are studied to demonstrate advantages and disadvantages of them Among them, some methods deal with homogenous network, whereas some deals with heterogeneous In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other

Survey on clustering in heterogeneous and homogeneous wireless sensor networks

A survey on unequal clustering protocols in Wireless Sensor Networks

Introducing mobility to Wireless Sensor Networks (WSNs) puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs). Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

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A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks

Display Omitted An Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) is proposed.UMBIC solves the hot spot problem and improves the lifetime of networks.UMBIC adjusts the intra-cluster and inter-cluster energy dissipation of clusters.UMBIC utilizes the multi-objective immune algorithm to finds the optimum clusters.Simulation experiments were conducted in MATLAB correctly. In multi-hop routing, cluster heads near the base station act as relays for far cluster heads and thus will deplete their energy very quickly. Thus, hot spots in the sensor field result. This paper introduces a new clustering algorithm named an Unequal Multi-hop Balanced Immune Clustering protocol (UMBIC) to solve the hot spot problem and improve the lifetime of small and large scale/homogeneous and heterogeneous wireless sensor networks with different densities. UMBIC protocol utilizes the Unequal Clustering Mechanism (UCM) and the Multi-Objective Immune Algorithm (MOIA) to adjust the intra-cluster and inter-cluster energy consumption. The UCM is used to partition the network into clusters of unequal size based on distance with reference to base station and residual energy. While the MOIA constructs an optimum clusters and a routing tree among them based on covering the entire sensor field, ensuring the connectivity among nodes and minimizing the communication cost of all nodes. The UMBIC protocol rotates the role of cluster heads among the nodes only if the residual energy of one of the current cluster heads less than the energy threshold, as a result the time computational and overheads are saved. Simulation results show that, compared with other protocols, the UMBIC protocol can effectively improve the network lifetime, solve the hot spot problem and balance the energy consumption among all nodes in the network. Moreover, it has less overheads and computational complexity.

An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

This paper presents a new Genetic Algorithm-based Energy-Efficient adaptive clustering hierarchy Protocol (GAEEP) to efficiently maximize the lifetime and to improve the stable period of Wireless Sensor Networks (WSNs). The new protocol is aimed at prolonging the lifetime of WSNs by finding the optimum number of cluster heads (CHs) and their locations based on minimizing the energy consumption of the sensor nodes using genetic algorithm. The operation of the GAEEP is broken up into rounds, where each round begins with a set-up phase, when the base station finds the optimum number of CHs and assigns members nodes of each CH, followed by a steady-state phase, when the sensed data are transferred to CHs and collected in frames; then these frames are transferred to the base station. The performance of the GAEEP is compared with previous protocols using Matlab simulation. Simulation results show that GAEEP protocol improves the network lifetime and stability period over previous protocols in both homogeneous and heterogeneous cases. Moreover, GAEEP protocol increases the reliability of clustering process because it expands the stability period and compresses the instability period.

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A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks

Nabil Sabor

Papers

Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks

A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks

An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks

A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks