Hop-based routing protocol based on energy efficient Minimum Spanning Tree for wireless sensor network

Sensor nodes are small and low-cost portable devices that are connected wirelessly to form a wireless sensor network. Sensor nodes are typically powered by a chemical battery source that has a load-dependent finite lifetime. Most applications require wireless sensor networks to operate reliably for an extended period of time beginning with their deployment. To ensure both the longevity of a wireless sensor network via power management techniques and development of a self-powered sensor node, it is important to understand a sensor node’s operational energy consumption. It is well understood that wireless sensor network lifetime has a strong functional dependence on sensor node lifetime. This paper presents the development of a stochastic model to capture the expected energy consumption of a schedule-driven sensor node per cycle of operation by utilizing renewal theory, random stopping criteria, and Wald’s inequality. The model captures the expected energy consumed due to generic operation of a schedule-driven sensor node when an external event occurs (i.e., node level activities) and energy consumed due to carrier sense multiple access/collision avoidance medium access control protocol (i.e., retransmission attempts, channel listening, and other network level activities). The stochastic energy model is used to compute the expected lifetime and cycle lifetime of the sensor node. This paper also computes the deterministic energy bounds corresponding to the minimum and maximum energy consumption of a sensor node per cycle of operation. Analytical and numerical model validations are performed and presented.

In the era of New Generation Networks (NWGN) many emerging and future enable technologies accomplish the requirements of ubiquitous communication networks. Mobile wireless sensor network (mWSN) is one of the rising and emerging technologies for various application of NWGN, the enormous concerns of these networks are data gathering with energy efficiency. Routing protocol act as middleware, which is responsible to enhance the network performance with less energy consumption. In this paper we propose, analyze and validate mobile data collector (MDC) based LEACH routing protocol for environmental application which is based on multi-hop routing strategy. MDC based LEACH routing protocol utilizes self-organized sensor nodes with distributed cluster formation technique, randomly selection of cluster heads to equally balance the energy consumption among the sensor nodes and finally forward the data to base station by the support of MDC. Moreover, our approach shows the significant improvement in terms of energy consumption of sensor nodes, enhance network lifetime and data gathering at base station are outperform when compared with LEACH protocol. Key words: Mobile wireless sensor networks, wireless sensor networks, network protocol, cluster based routing, data gathering technique.

Sensors are capable of performing different tasks such as receiving various environmental information and have generated the idea of creating and expanding the traditional networks to Wireless Sensor Networks (WSN). A WSN include various sensor nodes which are distributed in the environment to gather data. The sensor node’s location is not necessarily defined and not specified. Such a feature allows us to release them in hazardous or inaccessible places. On the other hand, this means that the WSN protocols and algorithms must have the ability to self-deny. Other unique features of WSNs are the ability of sensor nodes to collaborate and coordinate with each other. A processor is assigned to each sensor node which initially performs a series of acquired simple information processing to transmits the semi-processed data instead of sending all raw data to the center, Base Station (BS) or the sink node. Fault detections in WSN are of particular importance due to unique technical and conceptual challenges. In this paper, due to the specific limitations and operating conditions of wireless sensor networks, different fault detection algorithms were examined. The best way to improve the ability of fault detection is to reduce energy consumption in sensor nodes. In such techniques, using the residue number system in the wireless sensor network structure improves repair and fault detection. Moreover, modules are attached whenever we require distinguishing and correcting the information security in the number system. Although there are various algorithms yet the Residue Number System algorithm has higher performance.

Internet Protocol version 6 (IPv6) over Low-power Personal Area Network (6LoWPAN) is proposed by the Internet Engineering Task Force (IETF) working group to accomplish the concept of IP-based Wireless Sensor Network (WSN). A new layer is incorporated between IPv6 network layer and 802.15.4 MAC layer, which is entitled adaptation layer. Header compression, packet fragmentation, and layer two forwarding are the main tasks of the adaptation layer. An IPv6 packet is too big regarding to the Maximum Transmission Unit (MTU) size of 802.15.4 standard thus it has to be fragmented. Breaking up a big packet to number of small fragments and attaching new headers to each fragment may affect the energy level. In this paper, we place an attention on the impact of the adaptation layer on the energy consumption of a 6LoWPAN sensor node. From our analysis, we found that the adaptation layer, in particular the fragmentation process may increase the energy consumption of a sensor node by 5 to 10 percent.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

The ability of wireless sensor network (WSN) can be observed in different fields like patient wearable monitoring, health applications, intruder detection, combat monitoring and battlefield surveillance in the last few years. In such applications, WSN contains a wide range of sensor nodes, which will monitor continuously, and passes the data to end-user through base station (BS) or sink node (SN). In such applications where users are unattended to replace the sensor node frequently, that indicates users can expect long-lasting of WSN. So in this paper, we concentrated on long-lasting of WSN, by minimizing the energy utilization of sensor node in WSN. So we are applying the clustering technique. So, clustering will minimize the energy usage of the sensor node, such that it will improve the lifetime of WSN. In this, we introduced our proposed protocol “Low Energy Utilization with Dynamic Cluster Head (LEU-DCH)” which reduces the energy consumption of sensor nodes. Besides, we also have shown the best way of selecting how many clusters we need.

An efficient networks’ energy consumption and Quality of Services (QoS) are considered the most important issues, to evaluate the route quality of the designed routing protocol in Wireless Sensor Networks (WSNs). This study is presented an evaluation performance technique to evaluate two routing protocols: Secure for Mobile Sink Node location using Dynamic Routing Protocol (SMSNDRP) and routing protocol that used K-means algorithm to form Data Gathered Path (KM-DGP), on small and large network with Group of Mobile Sinks (GMSs). The propose technique is based on QoS and sensor nodes’ energy consumption parameters to assess route quality and networks’ energy usage. The evaluation technique is conducted on two routing protocols in two phases: The first phase is used to evaluate the route quality and networks’ energy consumption on small WSN with one mobile Sink Node (SN) and GMSs. The second phase, is used to evaluate the route quality and networks’ energy consumption on large network (four WSNs) with GMSs. The two phases are implementated by creating five sceneries via using NS2.3 simulator software. The implementation results of the proposed performance evaluation technique have demonstrated that SMSNDRP gives better networks’ energy consumption on small single network in comparison with KM-DGP. Also, it gives high quality route in large network that used four mobile SN, in contrast to KM-DGP that used sixteen mobile SNs. While in large network, it found that KM-DGP with sixteen mobile SNs gives better networks’ energy consumption in comparison with SMSNDRP with four mobile SNs.

Wireless Sensor Networks (WSNs) consist of a large number of small and low cost sensor nodes powered by small batteries and equipped with various sensing devices. Usually, for many applications, once a WSN is deployed, probably in an inhospitable terrain, it is expected to gather the required data for quite some time, say for years. Since each sensor node has limited energy, these nodes are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. There are two major approaches to sleep scheduling of sensor nodes, viz. (i) random (ii) synchronized. Any sleep scheduling scheme has to ensure that data can always be routed from source to sink. In this paper, we propose a novel approach for sleep scheduling of sensor nodes using a tree and an energy aware routing protocol which is integrated with the proposed sleep scheduling scheme. The tree is rooted at the sink node. The internal nodes of the tree remain awake and the leaf nodes are made to sleep. This provides an assured path from any node to the sink node. The tree is periodically reconstructed considering the remaining energy of each node with a view to balance energy consumption of nodes, and remove any failed nodes from the tree. The proposed approach also considerably reduces average energy consumption rate of each node as we are able to put more number of nodes to sleep in comparison to other approaches. Additional fault-tolerance is provided by keeping two paths from each node towards the sink. Extensive simulation studies of the proposed routing protocol has been carried out using Castalia simulator, and its performance has been compared with that of a routing protocol, called GSP, which incorporates sleep scheduling using random approach. The simulation results show that the proposed approach has longer network lifetime in comparison to that provided by GSP, and the energy consumption of nodes is also balanced.

Abstract. Motivated by the application of industry 4.0 and Internet of Things (IoT) technologies, the development of cyber physical systems (CPS) is gaining momentum. As CPS require multiple measurement technologies to drive the intended function, e.g., condition monitoring and in situ measurement, the integration of measurement systems into industrial processes or individual products becomes a critical activity within the development process. Development methods like the V-Model support developers with methodological guidelines, but the related methods and models do not provide sufficient information regarding the energy supply of embedded systems. If the measurement system, consisting of sensor, calculation and communication unit is integrated inside an either sealed or moveable system, e.g., in a gearbox, ensuring a reliable communication and energy supply is a challenging task. This contribution therefore focuses on the energy supply, in particular the electric power consumption of autarchic measurement systems, referred to as sensor nodes. Based on a literature review of existing physical principles determining the energy consumption of semiconductors, a simple estimation model is derived. Estimation models in the current literature mainly focus on the effects of source code or software in general without analyzing a possible impact of operation strategies, such as generic data processing logics in practical applications. The model presented in this contribution is therefore used to identify the energy consumption of sensor nodes influenced by ambient and operating conditions of sensor nodes. Strategies are examined experimentally using an exemplary sensor node, a climatic chamber and a sensor-integrated gearbox as the system to be observed. An analysis of the conducted experiments leads to a more precise model, which is evaluated regarding its significance for predicting the energy consumption and the underlying simplifications. Finally, general relations influencing the energy consumption are presented and necessary research suggested.

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.

Wireless sensor networks (WSNs) are limited to resources including computing power, storage capacity, and especially energy supply. Thus, energy consumption of sensor nodes has become a dominant performance index for a WSN. In addition, data transmission between sensor nodes is a main energy consumer of WSNs. This paper presents a method called immune genetic algorithm based multiple-mobile-agent itinerary planning (IGA-M2IP) that addresses issues of energy consumption in large-scale WSNs. The IGA-M2IP preserves a GA’s advantages, and further improves a GA’s efficiency by restraining possible degenerative phenomena during the evolutionary process.

Wireless sensor networks (WSN) are commonly formed by distributed sensor nodes equipped with wireless transmitter with limited bandwidth, computation power and battery sources. WSN are generally developed for wild life, military or environmental monitoring etc. These facts have highlighted challenges to minimize energy consumption of sensor nodes while still maintaining the goal of WSN developments. In this paper, an integration of cluster-based and position based routing protocols for multi-hop scenarios is proposed. The self-organized clusters are formed periodically where decision to become cluster head (CH) is performed individually by each node. Direct long communication between selected CH to sink node is avoided to preserve energy and minimize impact of overhearing transmission. Accordingly, a multi-hop communication scheme which adopts position based routing is applied. The proposed approach has been developed and tested using wireless simulation framework. It shows that the proposed approach can increase remaining battery energy, increase packet delivery ratio and reduce the number of dead nodes in various simulation settings.

The increased number of hazardous materials transportation accidents has caused a series of environmental and health problems across the world. In this paper, a hazardous materials transportation monitoring system (HMTM) is designed, implemented, and tested using multi-interface Wireless Sensor Networks (WSNs). In order to meet various state tanker transportation information collection, we designed a multi-interface sensor node. According to energy consumption and response time during clustering of Wireless Sensor Networks LEACH (Low Energy Adaptive Clustering Hierarchy) routing protocol, we proposed STATIC-LEACH routing protocol based on static clustering, it can effectively reduce energy consumption of the wireless sensor nodes and reduce network latency of cluster. The project is targeting a deployment on the vehicles. Ambient situation of the vehicles and also the state of the goods is monitored and is used to determine the safety status of the transportation.

In a wireless sensor network, reducing the energy consumption of sensor nodes and increasing energy conservation can be considered as two significant factors to prolong the network longevity. In this scheme, we proposed an energy-efferent data dissemination algorithm for the sensor nodes by making a virtual hexagonal cell-based infrastructure and multi-mobile sink to create a balance in energy consumption and to mitigate the energy-hole problem. The basic idea of the algorithm is to provide the ability for some of the sensing nodes that are elected by the virtual hexagonal backbone to inform other sensing nodes about the latest location of the nearest mobile sinks to decrease the overhead of updating the mobile sink location for the sensing nodes. Moreover, the sensor nodes can benefit considerably from these specified nodes as a relay node in the multi-hop routing process to send the data to the closest mobile sink based on the latest sink position information, which in turn leads to high energy conservation and low communication overhead between the sensing nodes and the mobile sinks in the network. According to the simulation results, the performance of the proposed scheme is better than the existing data transmission approaches in terms of total energy consumption, delay, and network longevity.

In the design and development of Wireless Sensor Networks (WSNs), one of the main challenges is to achieve maximal battery life-time, because of the constrained energy resources. The application of WSNs for real time monitoring contributes to minimization of potential production risks, emerging mainly from environmental influences and human actions. In the paper mathematical tools for optimization of the energy consumption of wireless sensor nodes while collecting distributed data in environmental parameters monitoring will be presented. In this contribution the stochastic optimization method-genetic algorithm is used to minimize the energy consumption of the wireless sensor nodes depending on the frequency of the transmitted data and the period of the transmission process. The optimization is conducted in cases of different scenarios: while the frequency of the transmitted data as well as the period of transmission of all the active components in a sensor node is increasing.