Energy Efficient Techniques in Wireless Sensor Networks

무선 센서 네트워크에서 에너지 효율성, 전송 지연 그리고 확장성은 중요한 요구사항이며, 특히 다수의 노드로 구성된 무선 센서 네트워크의 경우 네트워크 라이프타임 연장을 위해 제한된 배터리 전력 내에서 각 노드의 에너지 소비를 최소화 시켜야한다. 또한 전송률을 향상시키기 위해서는 각 센서 노드의 에너지 소비를 최소화하기 위한 효율적인 알고리즘과 에너지 관리 기술이 요구된다. 본 논문은 무선 센서 네트워크 환경에서 센서 데이터 전송을 위해 경로의 겹침이 없는 다중경로 프로토콜을 제안한다. 제안한 방법은 다중경로를 검색하고 라우팅 테이블에 다중경로를 추가시켜 센서 데이터 전송의 감소를 통해 네트워크 오버헤드를 최소화 시킨다. 제안한 라우팅 프로토콜은 각 노드의 에너지 소비를 최소화하고, 싱크 노드가 수신 강도 범위 내, 외부에 위치하더라도 센서 네트워크의 생명주기를 연장할 수 있다. 실제 데이터를 이용하여 현실 모델에 맞게 센서 네트워크를 구축하고 제안 기법의 타당성을 검증하며, 전체 에너지의 소비량을 평가한다. Energy efficiency, low latency and scalability for wireless sensor networks are important requirements, especially, the wireless sensor network consist of a large number of sensor nodes should be minimized energy consumption of each node to extend network lifetime with limited battery power. An efficient algorithm and energy management technology for minimizing the energy consumption at each sensor node is also required to improve transfer rate. Thus, this paper propose no-overlap multi-pass protocol provides for sensor data transmission in the wireless sensor network environment. The proposed scheme should minimize network overhead through reduced a sensor data translation use to searched multi-path and added the multi-path in routing table. Proposed routing protocol may minimize the energy consumption at each node, thus prolong the lifetime of the sensor network regardless of where the sink node is located outside or inside the received signal strength range. To verify propriety proposed scheme constructs sensor networks adapt to current model using the real data and evaluate consumption of total energy.

Wireless sensor network is consisting of power-limited sensors that sense the surrounding environment and transmit data to another sensor or to the particular gateway. Thus, a considerable amount of energy is dissipated which impact the overall performance and network lifetime. In order to mitigate the failure-based energy, routing and clustering mechanisms should be energy aware. Accordingly, and in the literature, many hierarchical clustering techniques are proposed in term to extend the network alive time such as LEACH and SEP. The role of the elected cluster head is the main objective of these techniques and appear the differences between them. In this paper, two energy efficient clustering techniques are evaluated in 100 uniformly distributed wireless sensor networks in term of number of alive nodes, residual energy, and stability period. The election of cluster head is achieved in heterogeneous conditions where some of the nodes are powered higher than others. The results show how the heterogeneity of wireless sensor network in term of energy remaining at nodes influences the overall performance and increase the stability region before first node is dying. SEP is outperformed LEACH in term of long stability zone by 4.2% when 20% of the nodes (m=0.2) are energized more energy by 100% (α=1). Moreover, when m=0.2 and α=.3, SEP has better stable region than LEACH by 26%. In general, the election of cluster head mechanism in LEACH clustering leads to reduce the stable region and the node die faster comparing to SEP. In which, the cluster head election is totally based on the remaining energy.

Wireless Sensor Networks (WSNs) ordinarily be composed of a large number of low-power sensor nodes which having several functions, that are a battery powered, and thus have very limited energy capacity. To lengthen the operational lifetime of a sensor network, energy efficiency should be considered in every aspect of sensor network design. In this paper, Enhanced Hierarchical Routing Technique (EHRT) is proposed to overcome the constraint of limited energy capacity of sensor nodes which enhancing the network lifetime and the energy efficiency. The suggested technique is a cluster-based routing which optimizes the low-energy adaptive clustering hierarchy routing technique (LEACH) by using a modified artificial fish swarm algorithm (AFSA). This modified AFSA selects the optimum clusters’ head (CHs) locations by applying a number of behaviors following, preying and swarming on each cluster separately and using a modified fitness function to compare these behaviors’ outputs to select the best CHs locations for each cluster separately. A framework for evaluating the performance is constructed and applied to verify the efficiency of the suggested technique comparing to other energy efficient routing techniques; optimized hierarchical routing technique (OHRT), low-energy adaptive clustering hierarchy (LEACH), and particle swarm optimized (PSO) routing techniques. The proposed technique yields best results than other techniques OHRT, LEACH, and PSO in terms of energy consumption and network lifetime. It reduces the energy dissipation by factor 0.7 compared with OHRT.

In this paper, we have proposed a hybrid adaptive coding and decoding scheme for multi-hop wireless sensor networks (WSNs). Energy consumption and transmission reliability are used as performance metrics for multi-hop communications in WSNs. The presented scheme takes into account distance, channel conditions and correction codes performance to decide coding and decoding procedure, and considers Reed Solomon code and Low Density Parity Check code to provide error protection on the transmitted data. The proposed approach aims to reduce the decoding power consumption and to prolong the lifetime of the network as well as improve the reliability of the transmission. Simulation results show that our performed scheme enhances both energy efficiency and communication reliability of multi-hop sensor networks.

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

The applications in wireless networks deem data gathering as an essential process that intends to increase the lifetime of the network by utilizing energy efficient techniques. Correspondingly, the energy efficiency and network lifetime in wireless sensor network can be enhanced by implementing an efficient technique, Clustering. This paper intends to minimize the energy and latency through efficient clustering protocol (ECP) architecture. In wireless micro-sensor networks, ECP architecture aims at achieving low energy dissipation and latency provided that application specific quality is not sacrificed. The ECP utilizes (i) randomized, adaptive, self - configuring cluster formation (ii) localized control for data transfers and (iii) application - specific data processing, for instance data aggregation or compression to achieve the goal. The cluster formation algorithm produces good clusters as the result by allowing each node to make autonomous decisions. The simulation results demonstrate that this algorithm also reduces overhead to the protocol by reducing the energy and latency for cluster formation.

Wireless sensor networks (WSNs) are used in several pervasive and ubiquitous applications such as security, health care, agriculture, environment and habitat monitoring. WSNs consist of a large number of sensor nodes which contain a few traditional sensors such as temperature, light, moisture, mobility, and pressure. Further, these wireless sensors are equipped with limited computing, storage, and communication resources. For extended network monitoring and easy maintenance, energy efficient data dissemination techniques are needed. This tutorial identifies the key challenges, techniques, and algorithms for energy efficient communication in WSNs. It would be beneficial for researchers, practitioners, and students. The tutorial will cover the following topics: Introduction to WSNs, coverage and connectivity, cross layer optimization, virtual antenna arrays, MAC layer, routing, algorithms, and applications.

Wireless sensor network (WSN) communication has gathered a lot of attention of research scholars due to its various features such as high wireless data transmission. A large number of techniques have been developed till now in order to achieve an energy efficient network. The clustering and cluster head selection is the major and difficult task to perform in a network. LEACH serves as a basic for rest of the energy efficient clustering protocols. This study considers the LEACH-Mobile Fuzzy (LEACH-MF) as base for developing the proposed work. Fuzzy Inference System (FIS) with LEACH along with threshold based data transmission concept is developed in this work. The major objective of this work is to utilize the allotted energy to sensor nodes in an effective way. The proposed model is parted in two forms i.e. Modified Parameter-LEACH-MF (MP-LEACH-MF) and Limited Communication-LEACH-MF (LC-LEACH-MF). LC-LEACH-MF is a reactive protocol whereas the former one is periodic. In order to assure the performance efficiency of the proposed work, the parameters such as Packet Delivery Ratio (PDR), Last Node Dead (LND), Half Node Dead (HND), First Node Dead (FND), Energy Consumption of the network are evaluated and along with this a comparison analysis has been done with traditional LEACH, LEACH–Mobile (LEACH-M), LEACH-MF. After analyzing the obtained results it is concluded that the LC-LEACH-MF outnumbers the rest of the traditional energy efficient clustering techniques.

Security and Energy efficiency are two key factors to be contemplated in the design of applications based on wireless sensor networks (WSNs). Optimization of energy consumption is obligatory for an increased life time of the network. Without security, attackers can disrupt the entire operation of sensor network by instigating diverse attacks like message tampering, message dropping either in partial or whole and message flooding etc. This work proposes a secure energy efficient attack resilient routing technique for zone based wireless sensor network with proactive detection of malicious zones and mitigation from the attacks. Different from earlier works on detecting each malicious node, this work cleaves the network to zones and allots a probabilistic fuzzy score to model the success ratio of packet propagation through the zone. The routing is adaptive to ongoing residual energy and security risks. A Firm decision cannot be made on the frameworks influencing the life time of the network considering it may influence the operations of network. Experimentation of the proposed solution is done in NS2 and contrasted with the existing solutions to prove the effectiveness of the approach.

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Energy efficiency is a key concern and challenging research issue during the design of routing algorithms for wireless sensor networks (WSNs). In order to effectively improve the energy utilization of each sensor node and network performance, mobility technology has been widely concerned. Since the implement of mobile sink node is relatively easier than normal sensor nodes, sink mobility technology has attracted much research interest in recent years. In this paper, we propose a mobile-sink routing algorithm based on energy and distance for WSNs. Trajectories of mobile sink nodes is predictable to improve energy efficiency of sensor networks. We first lay emphasis on the study of the selection of mobile sink node number and the selection of park positions, as well as their impact on energy consumption and network lifetime. Simulation results show that both the mobile sink nodes number and park positions have great influence on network performance, and our algorithm outperforms random movement and LEACH algorithm in terms of performance like energy consumption.

As the nodes have limited battery power in Wireless Sensor Networks (WSNs), energy efficiency and fault tolerance should be the two major issues in designing WSNs. However, previous studies on positioning base stations (BSs) in WSNs are focused on energy efficiency only. Yet, mission-critical applications like emergency medical care systems should be guaranteed continuous services considering fault tolerance. In this paper we propose to place multiple BSs considering not only energy efficiency but also fault tolerance. We present two strategies to find the optimal position of BSs; (1) minimizing the average transmission energy for energy efficiency; and (2) minimizing additional energy consumption after a BS failure for fault tolerance. The optimal positions for multiple BSs are derived by the metric that considers both energy efficiency and fault tolerance, with a weight factor. Our simulation results show that fault tolerance is important and strongly related to elongation of network lifetime. In addition, we show that our proposed scheme is more energy-effective than previously suggested strategies on unexpected environmental changes which occur commonly in WSNs and sustain the network lifetime effectively under BS failures.KeywordsSensor NodeWireless Sensor NetworkFault ToleranceNetwork LifetimeOptimal PositionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The growing needs to support efficient data communications in wireless sensor networks (WSN) cannot be ignored. The resource constrained nature of WSN requires it to efficiently transmit the data with little consumption of network resources. We present a gossip based K - BackTrack (KBT) reliable data dissemination protocol for unstructured ad-hoc WSNs1. The goal of our proposed protocol is to ensure reliable data delivery and, at the same time, keeping the overhead associated with reliability low. The basic idea we employ is that each node to delay the acknowledgement message until it reaches Kth hop in the WSN. Then the Kth node then acknowledge the receipt of the message to the source directly. By increasing the value of K, we can minimize the network traffic. Moreover, the data dissemination in our proposed protocol exhibits low bandwidth cost (by minimizing the acknowledgement) and increases network lifetime (since critical sensors, ie. close to the control center are not overused). Through extensive experimental evaluation and comparison, our findings demonstrate that our protocol achieves significant improvements in energy efficiency and network lifetime.

Wireless Sensor Network (WSN) consists of small battery-powered sensor nodes with limited energy resources, with sensing, computation, and communications capabilities. One of the crucial issues in WSN is energy consumption thus, poor energy efficient routing. The lifetime of the entire network depending on the energy efficiency of the sensor network which finally requires an energy- efficient routing protocol. This paper provides energy-efficient routing by using a fuzzy logic approach in the clusterhead selection process that provides a completely non-probabilistic approach in order to balance the energy consumption of nodes and prolong the WSN lifetime. This approach uses three fuzzy variables: Residual energy of the nodes, distance to Base station and distance to the cluster-head. Simulations results show that the proposed algorithm improves energy consumption and significantly prolong the network lifetime in wireless sensor networks when compared with LEACH protocol.