Increasing Network Lifetime in an Energy-Constrained Wireless Sensor Network

International audienceEnergy in Wireless Sensor Networks is a scarce resource, therefore an energy-efficient management is required to increase the network lifetime. In this paper, we study the problem of optimal power allocation, taking into account the estimation of total signal-to-noise ratio (SNR) at the Fusion Center (FC). We consider that nodes transmit their data to the Fusion Center over quasi-static Rayleigh fading channels (QSRC). In order to analyze our approach, we will investigate first the orthogonal channels, and secondly the non-orthogonal ones introducing a virtual MISO in the communication. We consider in both cases that the nodes have Channel State Information (CSI). Simulations that have been conducted using these two channel configurations show that, thanks to our new algorithm, the network lifetime is extended by an average that can reach 82,80% compared to the network lifetime in the other methods

An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks

Energy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period

Prolonging Network Lifetime of Clustered Wireless Sensor Networks

Wireless Sensor Networking is envisioned as an economically viable paradigm and a promising technology because of its ability to provide a variety of services, such as intrusion detection, weather monitoring, security, tactical surveillance, and disaster management. The services provided by wireless senor networks (WSNs) are based on collaboration among small energy-constrained sensor nodes. The large deployment of WSNs and the need for energy efficient strategy necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has been proven to provide the required scalability and prolong the network lifetime. Due to the bottle neck phenomena in WSNs, a sensor network loses its connectivity with the base station and the remaining energy resources of the functioning nodes are wasted. This thesis highlights some of the research done to prolong the network lifetime of wireless sensor networks and proposes a solution to overcome the bottle neck phenomena in cluster-based sensor networks. Transmission tuning algorithm for a cluster-based WSNs is proposed based on our modeling of the extra burden of the sensor nodes that have direct communication with the base station. Under this solution, a wireless sensor network continues to operate with minimum live nodes, hence increase the longevity of the system. An information theoretic metric is proposed as a cluster head selection criteria for breaking ties among competing clusters, hence as means to decrease node reaffiliation and hence increasing the stability of the clusters, and prolonging the network lifetime. This proposed metric attempts to predict undesired mobility caused by erosion

Optimizing communication and computation for multi-UAV information gathering applications

Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme

Analyzing the energy efficient path in Wireless Sensor Network using Machine Learning

As the sensor nodes are energy constrained, an important factor for successful implementation of a Wireless Sensor Network (WSN) is designing energy efficient routing protocols and improving its lifetime. Network life time has been described in many ways such as   the time when the network lost its connectivity or the time when the first node gets disconnected. Whatever may be the description, the main focus of many researchers is to design algorithms that enable the network to perform continuously for a longer duration. So, improving the energy efficiency and increasing the network lifetime are the two key issues in WSN routing. Because of the intelligent nature and learning capacity, reinforcement learning (RL) algorithms are very suitable for complex distributed problems such as routing in WSN. RL is a subclass of Machine Learning techniques.  It can be used to choose the best forwarding node for transmitting data in multipath routing protocols. A survey has been made in this paper regarding the implementation of RL techniques to solve routing problems in WSN. Also, an algorithm has been proposed which is a modified version of original Directed Diffusion (DD) protocol. The proposed algorithm uses Q-learning technique which is a special class of RL. Also, the significance of balancing the exploration and exploitation rate during path finding in Q-learning has been demonstrated using an experiment implemented in python. The result of the experiment shows that if exploration-exploitation rate is properly balanced, it always yields an optimum value of the reward and thus path found from source to the destination is efficient

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms

Distributed Optimal Rate-Reliability-Lifetime Tradeoff in Wireless Sensor Networks

The transmission rate, delivery reliability and network lifetime are three fundamental but conflicting design objectives in energy-constrained wireless sensor networks. In this paper, we address the optimal rate-reliability-lifetime tradeoff with link capacity constraint, reliability constraint and energy constraint. By introducing the weight parameters, we combine the objectives at rate, reliability, and lifetime into a single objective to characterize the tradeoff among them. However, the optimization formulation of the rate-reliability-reliability tradeoff is neither separable nor convex. Through a series of transformations, a separable and convex problem is derived, and an efficient distributed Subgradient Dual Decomposition algorithm (SDD) is proposed. Numerical examples confirm its convergence. Also, numerical examples investigate the impact of weight parameters on the rate utility, reliability utility and network lifetime, which provide a guidance to properly set the value of weight parameters for a desired performance of WSNs according to the realistic application's requirements.Comment: 27 pages, 10 figure

Movement-efficient Sensor Deployment in Wireless Sensor Networks

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Two key issues in MWSNs - energy consumption, which is dominated by sensor movement, and sensing coverage - have attracted plenty of attention, but the interaction of these issues is not well studied. To take both sensing coverage and movement energy consumption into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment to maximize the sensing coverage with specific energy constraints. We derive necessary conditions to the optimal sensor deployment with (i) total energy constraint and (ii) network lifetime constraint. Using these necessary conditions, we design Lloyd-like algorithms to provide a trade-off between sensing coverage and energy consumption. Simulation results show that our algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure