Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

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

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

Clustering and routing in WSNs are two well-known optimization problems that are classified as Non-deterministic Polynomial (NP)-hard. In this paper, we propose a single multi-objective problem formulation tackling these two problems simultaneously with the aim of finding the optimal network configuration. The proposed formulation takes into consideration the number of Cluster Heads (CHs), the number of clustered nodes, the link quality between the Cluster Members (CMs) and CHs and the link quality of the constructed routing tree. To select the best multi-objective optimization method, the formulated problem is solved by two state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs), and their performance is compared using two well-known quality indicators: the hypervolume indicator and the Epsilon indicator. Based on the proposed problem formulation and the best multi-objective optimization method, we also propose an energy efficient, reliable and scalable routing protocol. The proposed protocol is developed and tested under a realistic communication model and a realistic energy consumption model that is based on the characteristics of the Chipcon CC2420 radio transceiver data sheet. Simulation results show that the proposed protocol outperforms the other competent protocols in terms of the average consumed energy per node, number of clustered nodes, the throughput at the BS and execution time

A Full Area Coverage Guaranteed, Energy Efficient Network Configuration Strategy for 3D Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), providing full area coverage while maintaining connectivity between the sensors is considered an important issue. Coverage-aware sleep scheduling is an efficient way to optimize the coverage of WSNs while maximizing the energy consumption. On the other hand, clustering can provide an efficient way to achieve high connectivity in WSNs. Despite the close relationship between the coverage problem and the clustering problem, they have been formulated, discussed and evaluated separately. Furthermore, most existing WSN strategies are designed to be applied on Two-Dimensional (2D) fields under an ideal energy consumption model that relies on calculating the Euclidean distance between any pair of sensors. In reality, sensors are mostly deployed in a Three-Dimensional (3D) field in many applications and they do exhibit a discrete energy consumption model that depends on the sensors' status rather than the distance between them. In this paper, we propose a Pareto-based network configuration strategy for 3D WSN s. In the proposed protocol, deciding the status of each sensor in a 3D WSN s is formulated as a single multi-objective minimization problem. The proposed formulation considers the following combined properties: energy efficiency, data delivery reliability, scalability, and full area coverage. The performance of the proposed protocol is tested in 3D WSNs and under a realistic energy consumption model which is based on the characteristics of the Chip con CC2420 radio transceiver data sheet

MCSA: A multi-criteria shuffling algorithm for the MapReduce framework

During the shuffle stage of the MapReduce framework, a large volume of data may be relocated to the same destination at the same time. This, in turn, may lead to the network hotspot problem. On the other hand, it is always more effective to achieve better data locality by moving the computation closer to the data than the other way around. However, doing this may result in the partitioning skew problem, which is characterized by the unbalanced computational loads between the destinations. Consequently, shuffling algorithms should consider all the following criteria: data locality, partitioning skew, and network hotspot. In order to do so, we introduce MCSA, a Multi-Criteria shuffling algorithm for the MapReduce scheduling stage that rests on three cost functions to accurately reflect the trade-offs between these different criteria. Extensive simulations were conducted and their results show that the MCSA-based scheduler consistently outperforms other schedulers based on these criteria. Furthermore, the MCSA-based scheduler can be easily adjusted to the meet the distinct needs of different customers

Evolutionary-based coverage control mechanism for clustered wireless sensor networks

Many clustering protocols have been proposed for Wireless Sensor Networks (WSNs). However, most of these protocols focus on selecting the optimal set of Cluster Heads (CHs) in order to reduce or balance the network’s energy consumption and unfortunately, how to effectively cover the network area is often overlooked. Coverage optimization in WSNs is a well-known Non-deterministic Polynomial (NP)-hard optimization problem. In this paper, we propose a Genetic Algorithm (GA)-based Coverage Control Mechanism (GA-CCM) for clustered WSNs. GA-CCM provides an add-on mechanism that is designed to be integrated with any centralized clustering protocol to enhance its energy efficiency. GA-CCM finds the optimal set of active nodes that provides full area coverage and puts the redundant sensors into sleep mode to save energy. Extensive simulations of GA-CCM on 25 different WSNs topologies are conducted. Performance results are evaluated and compared against several well-known clustering protocols as well as a coverage-aware clustering protocol. Results show that GA-CCM always achieves full area coverage while minimizing the redundancy degree and the number of active nodes. To further evaluate the performance of GA-CCM as an add-on to existing clustering protocols, we integrate it with a Particle Swarm Optimization based CH selection protocol (PSO-CH), a comprehensive clustering protocol that considers many clustering objectives. To the best of our knowledge, PSO-CH has the lowest overall energy consumption among well-known clustering protocols. Experimental results show that this integration of GA-CCM to PSO-CH further improves its performance in terms of energy efficiency and packets delivery rate