An Energy Efficient, Load Balancing, and Reliable Routing Protocol for Wireless Sensor Networks

AN ENERGY EFFICIENT, LOAD BALANCING, AND RELIABLE ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS by Kamil Samara The University of Wisconsin-Milwaukee, 2016 Under the Supervision of Professor Hossein Hosseini The Internet of Things (IoT) is shaping the future of Computer Networks and Computing in general, and it is gaining ground very rapidly. The whole idea has originated from the pervasive presence of a variety of things or objects equipped with the internet connectivity. These devices are becoming cheap and ubiquitous, at the same time more powerful and smaller with a variety of onboard sensors. All these factors with the availability of unique addressing, provided by the IPv6, has made these devices capable of collaborating with each other to accomplish common tasks. Mobile AdHoc Networks (MANETS) and Wireless Sensor Networks (WSN) in particular play a major role in the backbone of IoT. Routing in Wireless Sensor Networks (WSN) has been a challenging task for researchers in the last several years because the conventional routing algorithms, such as the ones used in IP-based networks, are not well suited for WSNs because these conventional routing algorithms heavily rely on large routing tables that need to be updated periodically. The size of a WSN could range from hundreds to tens of thousands of nodes, which will make routing tables’ size very large. Managing large routing tables is not feasible in WSNs due to the limitations of resources. The directed diffusion algorithm is a well-known routing algorithm for Wireless Sensor Networks (WSNs). The directed diffusion algorithm saves energy by sending data packets hop by hop and by enforcing paths to avoid flooding. The directed diffusion algorithm does not attempt to find the best or healthier paths (healthier paths are paths that use less total energy than others and avoid critical nodes). Hence the directed diffusion algorithm could be improved by enforcing the use of healthier paths, which will result in less power consumption. We propose an efficient routing protocol for WSNs that gives preference to the healthier paths based on the criteria of the total energy available on the path, the path length, and the avoidance of critical nodes. This preference is achieved by collecting information about the available paths and then using non-incremental machine learning to enforce path(s) that meet our criteria. In addition to preferring healthier paths, our protocol provides Quality of Service (QoS) features through the implementation of differentiated services, where packets are classified as critical, urgent, and normal, as defined later in this work. Based on this classification, different packets are assigned different priority and resources. This process results in higher reliability for the delivery of data, and shorter delivery delay for the urgent and critical packets. This research includes the implementation of our protocol using a Castalia Simulator. Our simulation compares the performance of our protocol with that of the directed diffusion algorithm. The comparison was made on the following aspects: • Energy consumption • Reliable delivery • Load balancing • Network lifetime • Quality of service Simulation results did not point out a significant difference in performance between the proposed protocol and the directed diffusion algorithm in smaller networks. However, when the network’s size started to increase the results showed better performance by the proposed protocol

Cluster-based Routing Protocols for Energy-Efficiency in Wireless Sensor Networks

22 page

Hierarchical routing protocols for wireless sensor network: a compressive survey

Wireless Sensor Networks (WSNs) are one of the key enabling technologies for the Internet of Things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocols and a comparison of the different versions presented in the literature

Design Methods for the Cluster Head Selection in WSNs based on Node Residual Status to Enhance the Lifetime and Performance of the Network

This study introduces a novel approach to enhance Wireless Sensor Networks (WSNs) by proposing an energy-balancing algorithm and a unique Cluster Head (CH) selection strategy based on nodes' residual energy states. Unlike conventional methods, our algorithm dynamically distributes energy across nodes, mitigating localized energy depletion and extending the overall network lifetime. The Knapsack method optimizes resource allocation considering energy constraints. Performance evaluations, conducted using NS2.34/2.35, compare our approach with a widely used energy-balancing technique in WSNs. Our results showcase significant improvements. Notably, our algorithm achieves a remarkable 20% increase in network lifetime and a 25% enhancement in data throughput, demonstrating its effectiveness in optimizing resource usage. Furthermore, a 30% reduction in latency ensures faster and more responsive data transmission. The CH selection method positively impacts network coverage, resulting in a substantial 20% expansion of monitored areas. Compared to the existing technique, our proposed strategy consistently outperforms in key metrics, indicating its superior efficacy. This research contributes to advancing WSN sustainability and efficiency, particularly in scenarios prioritizing energy efficiency. The proposed algorithm emerges as a promising solution, demonstrating its potential to optimize WSN performance and longevity. Furthermore, the network simulator (NS) is used to examine the performance of the network with the NS2.34/2.35 version. The results exhibit the dominance of the projected method compared to existing techniques. Additionally, the method shows adaptability to dynamic network conditions, ensuring effective CH reselection in the presence of node failures or energy fluctuations

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig