Data collection algorithm for wireless sensor networks using collaborative mobile elements

The simplest approach to reduce network latency for data gathering in wireless sensor networks (WSN) is to use multiple mobile elements rather than a single mobile sink. However, the most challneging issues faced this approach are firstly the high network cost as a result of using large number of mobile elements. Secondly, it suffers from the difficulty of network partitioning to achieve an efficient load balancing among these mobile elements. In this study, a collaborative data collection algorithm (CDCA) is developed. Simulation results presented in this paper demonstrated that with this algorithm the latency is significantly reduced at small number of mobile elements. Furthermore, the performance of CDCA algorithm is compared with the Area Splitting Algorithm (ASA). Consequently, the CDCA showed superior performance in terms of network latency, load balancing, and the required number of mobile elements

FPOA Implementation for WSN Energy Efficient Routing

In this paper,a soft computing technique Flower Pollination optimization Algorithm(FPOA) for WSN is proposed.The Sensor Network is heterogeneous in nature. Proposed algorithm is designed and implemented in MATLAB.In this technique some nodes send data directly to base station as local pollination and some by Multihop Routing as global pollination. A routing scheme is process which helps in minimizing the energy consumption. We implemented FPOA and compared the results with techniques that are already developed.(Low Energy adaptive clustering hierarchy (LEACH), Stable Election Protocol (SEP) and Zonal-Stable Election Protocol (Z-SEP) Simulation results show that FPOA enhance first node dead time, throughput and overall energy consumes less than existing protocols like LEACH, SEP and Z-SE

Load-balancing rendezvous approach for mobility-enabled adaptive energy-efficient data collection in WSNs

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs

Efficient approach for maximizing lifespan in wireless sensor networks by using mobile sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out

The Beauty of the Commons: Optimal Load Sharing by Base Station Hopping in Wireless Sensor Networks

In wireless sensor networks (WSNs), the base station (BS) is a critical sensor node whose failure causes severe data losses. Deploying multiple fixed BSs improves the robustness, yet requires all BSs to be installed with large batteries and large energy-harvesting devices due to the high energy consumption of BSs. In this paper, we propose a scheme to coordinate the multiple deployed BSs such that the energy supplies required by individual BSs can be substantially reduced. In this scheme, only one BS is selected to be active at a time and the other BSs act as regular sensor nodes. We first present the basic architecture of our system, including how we keep the network running with only one active BS and how we manage the handover of the role of the active BS. Then, we propose an algorithm for adaptively selecting the active BS under the spatial and temporal variations of energy resources. This algorithm is simple to implement but is also asymptotically optimal under mild conditions. Finally, by running simulations and real experiments on an outdoor testbed, we verify that the proposed scheme is energy-efficient, has low communication overhead and reacts rapidly to network changes

Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performanc