Limited Network Lifetime Research Articles

A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.

An Efficient Processing Scheme for Concurrent Applications in the IoT Edge

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks

Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor performance and a limited network lifetime. To overcome these issues, we propose a depth controlled with energy-balanced routing protocol, which will be able to adjust the depth of lower energy nodes and be able to swap the lower energy nodes with higher energy nodes to ensure consistent energy utilization. The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. In the proposed energy-efficient routing protocol, an enhanced cluster head node is used to select a strategy that can analyze the remaining energy and directions of each participating node. In the simulation, the proposed model achieves 86.7% packet delivery ratio, 12.6% energy consumption, and 10.5% packet drop ratio over existing depth-based routing and energy-efficient depth-based routing methods for underwater wireless sensor network.

A reliable and secure multi-path routing strategy for underwater acoustic sensor networks

Underwater Acoustic Sensor Networks (UASNs) have nowadays become an attractive topic in scientific studies and commercial applications. An important challenge in UASN's design is the limited network lifetime and low reliability caused by the limited battery energy of sensor nodes and harsh channel conditions in underwater environments. In addition, sensor nodes may generate sensitive data, which needs to be concealed. To this end, cryptographic encryption is a commonly used method to cipher a data before transmission to maintain security. However, encryption methods require additional computation and extra energy, which causes a decrease in the network lifetime. To this end, transmitting fragmented data through multiple paths can be used as a security countermeasure, in conjunction with encryption against silent listening attacks. To address these challenges, in this study, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption and data fragmentation on network lifetime. In addition to an optimal solution, Simulated Annealing, Golden Section Search and Genetic Algorithm-based heuristic methods have been developed. Performance results show that the proposed approach jointly solves the problem of UASN lifetime maximization, while providing network reliability and security.

IDBR: Iot Enabled Depth Base Routing Method for Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) gained the attention of researchers due to their substantial applications in various fields. The major application areas of UWSN are environmental monitoring, underwater oil and gas extraction and military surveillance, smart farming, communication, and others. However, UWSNs are also prone to significant issues, such as limited network lifetime, the low processing capability of nodes, high energy consumption to run routing protocols, and difficult node replacement. Therefore, enhancing the lifetime of UWSN by reducing energy consumption and processing is a research issue. In this research, we proposed the IoT enabled depth base routing method (IDBR) to utilize energy efficiently. The performance of the proposed IDBR method is compared with conventional DBR protocol using simulation conducting in MATLAB. The performance of both the methods (i.e., IDBR and DBR) is evaluated in network energy consumption, the number of alive nodes, sink utilization, and end‐to‐end delay. The simulation‐based experiment results show that IDBR consumes 27.7% less energy and increases network stability than the DBR. Similarly, the utilization of the surface sinks in IDBR is more as compared to DBR as in IDBR, and sinks work as relay forward data to the base station without processing which increases the power of field nodes. The proposed mechanism improves the network’s lifetime and increases the accessibility and security of the sensed data.

Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

An energy efficient load balanced cluster-based routing using ant colony optimization for WSN

PurposeWireless sensor networks (WSNs) have emerged as one of the most promising technology in our day-to-day life. Limited network lifetime and higher energy consumption are two most critical issues in WSNs. The purpose of this paper is to propose an energy-efficient load balanced cluster-based routing protocol using ant colony optimization (LB-CR-ACO) which ultimately results in enhancement of the network lifetime of WSNs.Design/methodology/approachThe proposed protocol performs optimal clustering based on cluster head selection weighing function which leads to novel cluster head selection. The cluster formation uses various parameters which are remaining energy of the nodes, received signal strength indicator (RSSI), node density and number of load-balanced node connections. Priority weights are also assigned among these metrics. The cluster head with the highest probability will be selected as an optimal cluster head for a particular round. LB-CR-ACO also performs a dynamic selection of optimal cluster head periodically which conserves energy, thereby using network resources in an efficient and balanced manner. ACO is used in steady state phase for multi-hop data transfer.FindingsIt has been observed through simulation that LB-CR-ACO protocol exhibits better performance for network lifetime in sparse, medium and dense WSN deployments than its peer protocols.Originality/valueThe proposed paper provides a unique energy-efficient LB-CR-ACO for WSNs. LB-CR-ACO performs novel cluster head selection using optimal clustering and multi-hop routing which utilizes ACO. The proposed work results in achieving higher network lifetime than its peer protocols.

Software Defined Networking for Energy Harvesting Internet of Things

Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. The overall network performance of existing IoT is restricted by limited network lifetime. Hence, energy harvesting technology with energy replenishment from mobile charger is proposed to prolong the network lifetime. Energy harvesting IoT is emerged. Nodes can not only request energy replenishment from the mobile charger, but also transfer surplus energy to the mobile charger for improving energy utilization. This gives rise to bidirectional energy flows in the network. A new paradigm that energy flows coexist with data flows is further resulted in. But there exist great challenges on controlling these flows. Toward centralized flow control, we exploit software defined networking to simplify and optimize network management, thus introduce software defined energy harvesting IoT (SEANET). In our proposed architecture, the data plane, energy plane, and control plane are decoupled to support enhanced communications and flexible energy scheduling. We consider reliable communications for SEANET, and propose to relay data packets among the nodes with high reputation values and sufficient energy. In particular, reputation values of nodes are computed by the multiweighted subjective logic for higher accuracy. Besides, a Nash bargaining game is formulated to solve the benefit allocation problem for energy trading in SEANET. Numerical results indicate that SEANET improves data traffic by reducing packet loss, optimizes energy utilization, and saves energy.

Improving network lifetime by minimizing energy hole problem in WSN for the application of IoT

The world today is at the Internet of Things (IoT) inflection point with more number of products adding to its intelligence system through a wide range of connectivity. Wireless sensor Networks (WSN) have been very useful in IoT application for gathering and processing of data to the end user. However, limited battery power and network lifetime are few of the major challenges in the designing process of any sensor network. One of those is the Energy Hole Problem (EHP) that arises when the nodes nearer to the sink or base station die out early due to excess load as compared to other nodes that are far away. This breaks the connection of the network from the sink which results in shortening the lifetime of the network. In this paper, a trade-off is maintained between network lifetime and power requirement by implementing a sleep-awake mechanism. With the help of MATLAB simulations, it is found that after applying the mechanism, the network lifetime was extended to almost 300 and 700 rounds for TEEN and LEACH protocol respectively. The results will be beneficial for the design process in WSN for IoT application.

Evaluation of Energy Consumption of Reactive and Proactive Routing Protocols in MANET

Mobile Ad hoc Network (MANET) is a distributed, infrastructure-less and decentralized network. A routing protocol in MANET is used to find routes between mobile nodes to facilitate communication within the network. Numerous routing protocols have been proposed for MANET. Those routing protocols are designed to adaptively accommodate for dynamic unpredictable changes in network's topology. The mobile nodes in MANET are often powered by limited batteries and network lifetime relies heavily on the energy consumption of nodes. In consequence, the lack of a mobile node can lead to network partitioning. In this paper we analyse, evaluate and measure the energy efficiency of three prominent MANET routing protocols namely DSR, AODV and OLSR in addition to modified protocols. These routing protocols follow the reactive and the proactive routing schemes. A discussion and comparison highlighting their particular merits and drawbacks are also presented. Evaluation study and simulations are performed using NS-2 and its accompanying tools for analysis and investigation of results.

A Novel Energy Entropy Based on Clusterhead Selection Algorithm for Wireless Sensor Networks

Energy is the primary constraint on designing wireless sensor networks (WSNs) practically, leading to limited network lifetime of WSN. Different communication protocols and algorithms are investigated to find ways to reduce power consumption. Cluster formation and clusterhead selection are critical issues in WSN, which can prolong the lifetime and also improve the network’s performance. In order to maintain the stability of clusters, energy of wireless sensor nodes and connectivity are taken as the basis of clusterhead election. We consider the problem of appropriate clusterhead selection in WSNs. This paper proposes a Novel Energy Entropy Based on Clusterhead Selection Algorithm for WSNs (EE-CSAW). The protocol constructs a new metric of node stability and selects a stable clusterhead with the help of entropy metric to reduce the number of clusterhead reconstruction. It selects the nodes which have the most weight and stability to be the clusterheaders. Simulations demonstrate the performance benefits of our proposal EE-CSAW over SLEACH and Thein’s proposed algorithm in WSNs.

An energy-efficient distributed clustering algorithm for heterogeneous WSNs

Wireless sensor networks (WSNs) were envisaged to become the fabric of our environment and society. However, they are yet unable to surmount many operational challenges such as limited network lifetime, which strangle their widespread deployment. To prolong WSN lifetime, most of the existing clustering schemes are geared towards homogeneous WSN. This paper presents enhanced developed distributed energy-efficient clustering (EDDEEC) scheme for heterogeneous WSN. EDDEEC mainly consists of three constituents i.e., heterogeneous network model, energy consumption model, and clustering-based routing mechanism. Our heterogeneous network model is based on three energy levels of nodes. Unlike most works, our energy consumption model takes into account the impact of radio environment. Finally, the proposed clustering mechanism of EDDEEC changes the cluster head selection probability in an efficient and dynamic manner. Simulation results validate and confirm the performance supremacy of EDDEEC compared to existing schemes in terms of various metrics such as network life.

An Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks –Edrleach

The Cluster-head Gateway Switch Routing protocol (CGSR) uses a hierarchical network topology. CGSR organizes nodes into clusters, with coordination among the members of each cluster entrusted to a special node named cluster-head. The cluster head selection is done with the help of any of the algorithm for cluster head selection. Energy is the primary constraint on designing any Wireless Networks practically. This leads to limited network lifetime of network. Low-Energy Adaptive Clustering Hierarchy (LEACH) and LEACH with deterministic cluster head selection are some of the cluster head algorithms that enable to optimize power consumption of WSN. There are various factors like density & distance, threshold based, power efficient. Load balancing and scalability are the other factors which plays important role in the selection of Cluster head. Algorithms based on load balancing reduce communication cost to a great extent. The algorithms that this study is focused are A Density and Distance based Cluster Head, An Energy Efficient Algorithm for Cluster-Head Selection in WSNs, Consumed Energy as a Factor for Cluster Head. These three algorithms are analyzed and studied in this paper. The analysis of these algorithms gave birth to a new algorithm called EDRLEACH, which is proposed through this paper. Keywords-Cluster head,energy efficient algorithms,selection head algorithms, wireless sensor,networks.

Wireless passive sensor networks

The primary challenge in wireless sensor network deployment is the limited network lifetime due to finite-capacity batteries. Hence, the vast majority of research efforts thus far have focused on the development of energy-efficient communication and computing mechanisms for WSNs. In this article a fundamentally different approach and hence completely new WSN paradigm, the wireless passive sensor network, is introduced. The objective of the WPSN is to eliminate the limitation on system lifetime of the WSN. In a WPSN power is externally supplied to the sensor network node via an external RF source. Modulated backscattering is discussed as an alternative communication scheme for WPSNs. The feasibility is investigated along with the open research challenges for reliable communication and networking in WPSNs.

On energy provisioning and relay node placement for wireless sensor networks

Wireless sensor networks that operate on batteries have limited network lifetime. There have been extensive recent research efforts on how to design protocols and algorithms to prolong network lifetime. However, due to energy constraint, even under the most efficient protocols and algorithms, the network lifetime may still be unable to meet the mission's requirements. In this paper, we consider the energy provisioning (EP) problem for a two-tiered wireless sensor network. In addition to provisioning additional energy on the existing nodes, we also consider deploying relay nodes (RNs) into the network to mitigate network geometric deficiencies and prolong network lifetime. We formulate the joint problem of EP and RN placement (EP-RNP) into a mixed-integer nonlinear programming (MINLP) problem. Since an MINLP problem is NP-hard in general, and even state-of-the-art software and techniques are unable to offer satisfactory solutions, we develop a heuristic algorithm, called Smart Pairing and INtelligent Disc Search (SPINDS), to address this problem. We show a number of novel algorithmic design techniques in the design of SPINDS that effectively transform a complex MINLP problem into a linear programming (LP) problem without losing critical points in its search space. Through numerical results, we show that SPINDS offers a very attractive solution and some important insights to the EP-RNP problem.

Resource aware sensor nodes in wireless sensor networks

Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput.