Sensor Activity Scheduling Protocol for Lifetime prolongation in Wireless Sensor Networks

In Wireless Sensor Network (WSN), the dense sensor nodes deployment in the sensing field can be exploited in conserving the energy of the whole network, where the data of these nodes can be highly correlated. Therefore, it is necessary to turn off the unnecessary nodes that sense similar sensor readings so as to reduce the redundant sensed readings and decrease the communication overhead thus extend the WSN lifetime. This article suggests a Sensor Activity Scheduling (SAS) protocol for lifetime improvement of WSNs. SAS works in a periodic way. It exploits the spatial correlation among sensed sensor data in order to produce the best sensor activities schedule in WSNs. SAS composed of three phases: data collection, decision-based optimization, and sensing. SAS measures the similarity degree among the sensed data that collected in the first phase. It makes a decision of which sensors stay active during the sensing phase in each period and put the other nodes into low power sleep whilst keeping a good accuracy level to the received data at the sink to conserve the power and enhance the lifetime of the WSN. Several experiments based on real sensed data and by using OMNeT++ simulator demonstrate that SAS can save energy and extend the WSN lifetime efficiently compared with the other methods

Maximizing lifetime of range-adjustable wireless sensor networks: a neighborhood-based estimation of distribution algorithm

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS

Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime

Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically towards the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance

Energy Consumption Model of WSN Based on Manifold Learning Algorithm

Energy saving is one of the most important issues in wireless sensor networks. In order to effectively model the energy consumption -in wireless sensor network, a novel model is proposed based on manifold learning algorithm. Firstly, the components of the energy consumption by computational equations are measured, and the objective function is optimized. Secondly, the parameters in computational equations are estimated by manifold learning algorithm. Finally, the simulation experiments on OPNET and MATLAB Simulink are performed to evaluate the key factors influencing the model. The experimental results show that the proposed model had significant advantage in terms of synchronization accuracy and residual energy in comparison with other methods

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC

Improved Unequal-Clustering and Routing Protocol

Increased network lifetime is a desired property of low-powered and energy-constrained Internet of Things (IoT) devices that are deployed in wireless network environments. Clustering is used as a technique in multiple solutions to improve overall network lifetime. Further variants in the clustering process are defined to optimize the results. One such variant is equal clustering, where all the clusters have the same size. However, this approach suffers from the issue of nodes closer to the base station (BS) dying out earlier. As an alternative, unequal clustering is proposed, where clusters close to the BS are of smaller size; thus, cluster heads (CHs) consume a substantial proportion of their energy for being acting as data forwarding nodes. In this paper, we propose an unequal clustering approach with the BS at the center of a circular area. The size of each cluster is fixed and computed based on the node density of the area. The number of clusters increases from outwards to inwards towards the BS. The results show considerable performance gain over selected benchmark works