A simple routing algorithms based on energy consumption

In this paper, we address the problem of multicast routing in mobile ad-hoc networks from the viewpoint of energy efficiency. In an ad-hoc wireless network, each node runs on a local energy source which has a limited energy life span. Thus, energy conservation is a critical issue in mobile ad-hoc networks. One approach for energy conservation is to establish routes that require lowest total energy consumption. This optimization problem is referred as the minimum-energy multicast routing problem (MEMRP). In this paper, propose new efficient heuristic algorithms for the construction of energy-efficient multicast routes in mobile ad-hoc networks. These algorithms exploit the broadcast nature of the wireless channel, and address the need for energy-efficient operations. Empirical studies show that our algorithms are able to achieve better performance than algorithms that have been developed for MEMRP.

Energy conservation is a critical issue in ad hoc wireless networks for node and network life, as the nodes are powered by batteries only. One major approach for energy conservation is to route a communication session along the route which requires the lowest total energy consumption. This optimization problem is referred to as Minimum-Energy Routing. While the minimum-energy unicast routing problem can be solved in polynomial time by shortest-path algorithms, it remains open whether the minimum-energy broadcast routing problem can be solved in polynomial time, despite the NP-hardness of its general graph version. Recently three greedy heuristics were proposed in [11]: MST (minimum spanning tree), SPT (shortest-path tree), and BIP (broadcasting incremental power). They have been evaluated through simulations in [11], but little is known about their analytical performances. The main contribution of this paper is a quantitative characterization of their performances in terms of approximation ratios. By exploring geometric structures of Euclidean MSTs, we have been able to prove that the approximation ratio of MST is between 6 and 12, and the approximation ratio of BIP is between 13/3 and 12. On the other hand, we show that the approximation ratio of SPT is at least n/2, where n is the number of receiving nodes. To the best of our knowledge, these are the first analytical results for the minimum-energy broadcasting problem.

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.

Wireless sensor networks contains small sensor node and processor which have several issues and challenges with regards to Limited Computational Capacity, Energy Efficiency, Path Planning Overhead and many more. Entire lifetime of network depends on efficient energy consumption in sensor network so it is a challenging task to design an Energy Efficient Routing Protocol for wireless sensor networks. Wireless sensor nodes dynamically self-organize themselves without utilizing preexisting infrastructure. Problem becomes more complex if some sensor node having mobility for some particular instance. In this paper we proposed an energy efficient routing protocol for WSN motivated by swarm intelligence of rummage behavior of ant and also covered mobility behavior of node. Furthermore we have combined the fuzzy logic with behavior of ant to take a better decision to find optimal path with less energy consumption. Our proposed algorithm utilize the principle of Fuzzy logic in addition of Ant Colony Optimization to develop energy efficient routing. The performance of our proposed work is evaluated on QualNet 7.4 Network Simulator. The simulation result shows that the proposed technique optimize the energy consumption, decrease RREQ packet and increase the lifetime of network as compare to existing routing protocol.

In this paper we study the minimum total energy and maximum network lifetime routing problem in wireless ad hoc networks. We develop competitive online schemes for an infinite sequence of random routing requests with provable approximation factors in both measures. In addition, we produce fundamental bounds on the expected total energy consumption and network lifetime in the optimal offline solution. Our results are verified through simulations. In the course of our analysis we derive an interesting property of shortest path trees (SPT) for a random point process. We show that the maximum edge length of any SPT is asymptotically equal to the maximum edge length of a minimum spanning tree. This result is of self interest for the study of energy efficient routing as many heuristics rely on SPTs.

Mobile Ad hoc Network (MANET) has mobile and battery operated nodes which can provide communication facility to users irrespective of any infrastructure and centralized control. It is a self-deployed and self-operated communication network having typical applications such as disaster relief, information exchange at conference or classroom and battle field communication etc. MANETs have many challenges such as limited battery power, lack of centralized control and security, the need for special routing protocols etc. Due to highly dynamic and dispersed nature of MANETs, routing becomes one of the important concerns in MANET. The network can get partitioned due to mobility and/or exhaustion of the nodes. Therefore, energy efficient routing is of great importance for the MANETs. This paper presents a simulation study of existing MANET routing protocols like AODV, DSR, and DSDV based on various metrics like packet delivery ratio (PDR), energy consumption, throughput, overheads, network lifetime and end to end delay to select a suitable protocol for further modification in order to improve the energy efficiency of the nodes. The simulation results show that the DSR protocol performs better than that of AODV and DSDV. Hence DSR protocol is modified based on an algorithm that considers the nodes' residual energy to extend the network lifetime. Implementation of modified protocol shows 22 % increase in the network lifetime and 13 % reduction in energy consumption.

In this paper, we propose to address the energy efficient routing problem in multi-hop wireless networks with accumulative relay. In the accumulative relay model, partially overheard signals of previous transmissions for the same packet are used to decode it using a maximal ratio combiner technique [J.G. Proakis, 2001]. Therefore, additional energy saving can be achieved over traditional energy efficient routing. The idea of accumulative relay originates from the study of relay channel in information theory with a main focus on network capacity. It has been independently applied to minimum-energy broadcasting in L.G. Manish Agrawal et al. (2004), I. Maric and R. Yates (2002). We formulate the minimum energy accumulative routing problem (MEAR) and study it. We obtain hardness of approximation results counterbalanced with good heuristic solutions which we validate using simulations. Without energy accumulation, the classic shortest path (SP) algorithm finds the minimum energy path for a source-destination pair. However, we show that with energy accumulation, the SP can be arbitrarily bad. We turn our attention to heuristics and show that any optimal solution of MEAR can be converted to a canonical form - wave path. Armed with this insight, we develop a polynomial time heuristic to efficiently search over the space of all wavepaths. Simulation results show that our heuristic can provide more than 30% energy saving over minimum energy routing without accumulative relay. We also discuss the implementation issues of such a scheme.

The sewage treatment industry intensively consumes energy, which results in high operating costs and aggravates the energy crisis in China to a certain extent. Therefore, the study of the energy consumption and conservation of sewage treatment processes is of high realistic significance. Domestic and overseas sewage treatment processes, their current status of energy consumption and energy audit, as well as the trend of sewage treatment processes and their energy conservation are explored to obtain theoretical enlightenment on the energy conservation, emission reduction, and sewage treatment recycling in China. The results show that the activated sludge process is mainly used in Chinese sewage treatment plants. The average energy consumption of all sewage treatments is 0.30 kWh/m3 in China. CASS achieved the lowest amount of average energy consumption at 0.24 kWh/m3. SBR obtained the largest energy consumption of 0.36 kWh/m3. The energy consumption is 0.06 kWh/m3 for sewage lifting, 0.178 kWh/m3 for sewage treatment, and 0.028 kWh/m3 for sludge treatment in China.

Orthogonal frequency-division multiplexing [OFDM] is an information transfer technique in which a single data flow is divided between several closely spaced narrowband subchannel frequency range rather than a single Wideband channel frequency. The information is sent to the relay node there is a delay and some data is lost in the relay node is the major issue in the existing system. To overcome these challenges, The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. In this paper, a novel Five Input Hybrid Optimization Relay Node Selection and Energy Efficient Routing (FIHORNSEER) technique has been proposed for choosing the best relay based on noises. Ant Lion Optimization (ALO) is initially utilized to select the relay node's elite position. Secondly, the Crow Search Optimization (CSO) Algorithm is used for the phenomenon of position and memory of each relay. Finally, the Memetic Algorithm (MA) was generated by integrating the Ant Lion and Crow search optimization algorithm for the best relay node selection. The proposed framework is compared with previous techniques like FRNSEER, LMMSE, and HABO-OFDM Methods in terms of performance analysis, such as average utility, Energy Consumption, and Network Life Time. The result shows that the proposed FIHORNSEER improves the energy consumption better than 22.01%, 16.4%, and 12.2% FRNSEER, LMMSE, and HABO-OFDM, respectively.

In ad hoc networks, energy conservation is a very important design issue. Several energy-efficient routing protocols are proposed to maximize the network lifetime which is defined as the time until the first node in the network runs out of energy. In this paper, we propose a Multipath Energy Efficient Routing (MEER) mechanism. We consider a network that is shared by a set of sources, each one communicates with its corresponding destination using multiple paths. MEER operates at each source node in a distributed manner and adaptively adjusts the traffic allocation among multiple paths with the objective of maximizing the network lifetime. We evaluate the performance of MEER by comparing it with some present energy efficient routing protocols via simulation. Satisfyingly, our proposal achieves the goal of more residual energy and better balanced energy residual, as a result the network lifetime is elongated.

Recent developments in wireless sensor networks (WSNs) have led to significant advances in energy-efficient routing protocols through the integration of deep learning and bio-inspired optimization algorithms. WSNs face critical challenges due to limited battery capacity, computational constraints, and dynamic network topology. This review article provides a comprehensive comparative analysis of state-of-the-art approaches for energy optimization in WSNs, focusing on clustering and routing protocols. We examine three major publications that propose hybrid frameworks combining deep learning architectures (CapsNet, ResNet-50) with metaheuristic optimization algorithms (ETSA, SOPSO, ACO). The analysis categorizes these protocols based on network architecture, energy models, optimization strategies, and performance metrics. We compare their effectiveness in extending network lifetime, reducing energy consumption, improving packet delivery ratio, and minimizing transmission delay. The comparative study demonstrates that hybrid deep learning-swarm intelligence approaches achieve 18-38% improvement in network lifetime, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2} \boldsymbol{-} \mathbf{1 4 \%}$</tex> reduction in energy consumption, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 5} \boldsymbol{-} \mathbf{2 4 \%}$</tex> lower delay compared to traditional protocols. This article serves as a comprehensive reference for researchers and practitioners working on nextgeneration energy-efficient WSN deployments.

In recent times, different routing protocols have been proposed in the Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) to explore the underwater environment for different purposes, i.e., scientific and military purposes. However, high Energy Consumption (EC), End to End (E2E) delay, low Packet Delivery Ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in Underwater Wireless Sensor Network (UWSN). The high E2E delay, EC and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. So, this paper presents two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy Efficient Routing (Im-CBE2R) protocol. At this end, both routing protocols minimize the probability of void hole occurrence and in return minimizes the EC and E2E delay. In both routing protocols, courier nodes are positioned at different strategic locations to keep the greedy forwarding continuous. The proposed routing protocols are also analyzed by varying the Packet Size (PS) and Data Rate (DR). Additionally, various simulations have been performed to authenticate the proposed routing protocols. Simulation results show that the proposed routing protocols outperform the baseline routing protocols in counterparts.

Appropriate clustering and routing algorithms in multi-hop wireless sensor networks considerably affect the energy consumption and prolong the network lifetime. Besides, the required inter-cluster communications in these networks may lead to imbalanced energy consumption among cluster-heads that in turn decreases the network lifetime. In this paper, a gradient-based clustering algorithm for energy-efficient routing (GCER) in wireless sensor networks is proposed. The main idea is based on partitioning the sensing region in such a way that the total energy consumption of the network is minimized. The algorithm uses unequal clustering structure which balances the energy consumption among the cluster-heads and lessens the effect of hotspot problem. Furthermore, a distributed protocol for choosing cluster-heads and routing is deployed to balance the energy among all sensor nodes. Simulation results show that the proposed scheme balances the energy consumption among the cluster-heads and increases the network lifetime compared to some recent reported clustering schemes in the literature.

China’s economy adopts an extensive growth method. This economic growth method relies on resource input, which consumes huge energy and causes excessive pressure on resources and the environment. The economic growth rate of Fujian Province ranks among the highest in the country in recent years, and its energy consumption is still on the high side. Industrial enterprises, especially the manufacturing industry, account for the majority of energy consumption. In order to ensure the sustainable development of enterprises and society, the government needs to make plans for energy conservation and consumption reduction, policy support and enterprise energy conservation and consumption reduction measures to promote energy conservation and consumption reduction in the whole society, and strive to achieve coordinated development of economic growth and environmental protection.

Energy is a critical and challenging issue for battery-constrained devices, especially for mobile ad hoc stations, which are responsible for relaying data packets for neighbor nodes. Therefore, considerable research has been devoted to the research of energy consumption efficiency. Care has been taken not only to reduce the overall energy consumption but also to balance individual battery usage, since unbalanced energy consumption will result in earlier node failure for overloaded nodes, and then lead to network partition and reduced network lifetime. This paper presents a novel on demand routing algorithm named energy aware reliable routing (EARR) to avoid the over usage of hot spots, reduce the route-reconstructions due to residual energy shortages, and finally prolong network lifetime. In the method, only nodes with sufficient residual energy to complete the task will take part in the propagation of route request (RREQ), therefore only valid path has the potential to transmit data. Simulation results verified that EARR can improve the network performance in terms of average route discovery time, normalized routing load, as well distribution of energy consumption. Besides, it is more robust to energy estimation error than existing methods.