We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

https://www.mdpi.com/1424-8220/16/3/414/pdf

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network

Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques

A power efficient cluster-based routing algorithm for wireless sensor networks

Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e., coverage-aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered.

/pdf/coverage-protocols-for-wireless-sensor-networks-review-and-4ds5g0oeu1.pdf

Coverage protocols for wireless sensor networks: Review and future directions

A Pareto optimization-based approach to clustering and routing in Wireless Sensor Networks

Clustering is an efficient topology control approach for maximizing the lifetime and scalability of Wireless Sensor Networks (WSNs). Many cluster-based routing techniques have been proposed in the literature. However, in most of the proposed protocols, the communication between a sensor node and its designated cluster head (CH) is assumed to be single-hop. Multi-hop communication can be used when the communication range of the sensor nodes is limited or the number of sensor nodes is very large in a network. Moreover, they used a predetermined percentage of CHs regardless of the network density or the number of live nodes. Clustering is known to be non-deterministic polynomial (NP)-hard problems for a WSN. Particle Swarm Optimization (PSO) is a swarm intelligent approach that can be applied for finding fast and efficient solutions of such problems. In this paper, we propose a novel centralized PSO protocol for Hierarchical Clustering (PSO-HC) in WSNs. Our objective is to maximize the network lifetime by minimizing the number of active CHs and to maximize the network scalability by using two-hop communication between the sensor nodes and their respective CHs. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN was investigated. Extensive simulations show that PSO-HC outperforms the well-known cluster-based sensor network protocols in terms of average consumed energy and throughput.

/pdf/pso-hc-particle-swarm-optimization-protocol-for-hierarchical-35ul3imm0y.pdf

PSO-HC: Particle swarm optimization protocol for hierarchical clustering in Wireless Sensor Networks

In Wireless Sensor Network (WSN), Clustering sensor nodes is an efficient topology control method to reduce energy consumption of the sensor nodes. Many link quality-based clustering techniques have been proposed in the literature. However, they assumed that each sensor node is equipped with a self-locating hardware such as GPS. Though this is a simple solution, the resulting cost renders that solution inefficient and unrealistic. Furthermore, several studies has shown that link quality in WSN is not correlated with distance. In addition to that, they used an energy model that is fundamentally flawed for modelling radio power consumption in sensor networks. They ignore the listening energy consumption, which is known to be the largest contributor to expended energy in WSN. Clustering is a Non-deterministic Polynomial (NP)-hard problem for a WSN. Particle Swarm Optimization (PSO) is a swarm intelligent approach that can be applied for finding fast and efficient solutions of such problem. In this paper, a PSO-based protocol is used to find the optimal set of cluster heads that maximize the network coverage, energy efficiency and link quality. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN was investigated. Numerical simulations demonstrate the effectiveness of the proposed protocol.

Riham S. Elhabyan

Papers

Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network

Coverage protocols for wireless sensor networks: Review and future directions

A Pareto optimization-based approach to clustering and routing in Wireless Sensor Networks

PSO-HC: Particle swarm optimization protocol for hierarchical clustering in Wireless Sensor Networks

Particle swarm optimization protocol for clustering in wireless sensor networks: A realistic approach