A COMMUNICATION FRAMEWORK FOR MULTIHOP WIRELESS ACCESS AND SENSOR NETWORKS: ANYCAST ROUTING & SIMULATION TOOLS

The reliance on wireless networks has grown tremendously within a number of varied application domains, prompting an evolution towards the use of heterogeneous multihop network architectures. We propose and analyze two communication frameworks for such networks. A first framework is designed for communications within multihop wireless access networks. The framework supports dynamic algorithms for locating access points using anycast routing with multiple metrics and balancing network load. The evaluation shows significant performance improvement over traditional solutions. A second framework is designed for communication within sensor networks and includes lightweight versions of our algorithms to fit the limitations of sensor networks. Analysis shows that this stripped down version can work almost equally well if tailored to the needs of a sensor network. We have also developed an extensive simulation environment using NS-2 to test realistic situations for the evaluations of our work. Our tools support analysis of realistic scenarios including the spreading of a forest fire within an area, and can easily be ported to other simulation software. Lastly, we us our algorithms and simulation environment to investigate sink movements optimization within sensor networks. Based on these results, we propose strategies, to be addressed in follow-on work, for building topology maps and finding optimal data collection points. Altogether, the communication framework and realistic simulation tools provide a complete communication and evaluation solution for access and sensor networks

Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks

Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.https://doi.org/10.3390/s15071616

Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.https://doi.org/10.3390/s14030507

An Energy Efficient Routing Protocol for Wireless Sensor Networks using A-star Algorithm

AbstractSensors are regarded as significant components of electronic devices. In most applications of wireless sensor networks (WSNs), important and critical information must be delivered to the sink in a multi-hop and energy-efficient manner. Inasmuch as the energy of sensor nodes is limited, prolonging network lifetime in WSNs is considered to be a critical issue. In order to extend the network lifetime, researchers should consider energy consumption in routing protocols of WSNs. In this paper, a new energy-efficient routing protocol (EERP) has been proposed for WSNs using A-star algorithm. The proposed routing scheme improves the network lifetime by forwarding data packets via the optimal shortest path. The optimal path can be discovered with regard to the maximum residual energy of the next hop sensor node, high link quality, buffer occupancy and minimum hop counts. Simulation results indicate that the proposed scheme improves network lifetime in comparison with A-star and fuzzy logic(A&F) protocol

QoS BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK

A Wireless Sensor Networks (WSN) is composed of a large number of low-powered sensor nodes that are randomly deployed to collect environmental data. In a WSN, because of energy scarceness, energy efficient gathering of sensed information is one of the most critical issues. Thus, most of the WSN routing protocols found in the literature have considered energy awareness as a key design issue. Factors like throughput, latency and delay are not considered as critical issues in these protocols. However, emerging WSN applications that involve multimedia and imagining sensors require end-to-end delay within acceptable limits. Hence, in addition to energy efficiency, the parameters (delay, packet loss ratio, throughput and coverage) have now become issues of primary concern. Such performance metrics are usually referred to as the Quality of Service (QoS) in communication systems. Therefore, to have efficient use of a sensor node’s energy, and the ability to transmit the imaging and multimedia data in a timely manner, requires both a QoS based and energy efficient routing protocol. In this research work, a QoS based energy efficient routing protocol for WSN is proposed. To achieve QoS based energy efficient routing, three protocols are proposed, namely the QoS based Energy Efficient Clustering (QoSEC) for a WSN, the QoS based Energy Efficient Sleep/Wake Scheduling (QoSES) for a WSN, and the QoS based Energy Efficient Mobile Sink (QoSEM) based Routing for a Clustered WSN. Firstly, in the QoSEC, to achieve energy efficiency and to prolong network/coverage lifetime, some nodes with additional energy resources, termed as super-nodes, in addition to normal capability nodes, are deployed. Multi-hierarchy clustering is done by having super-nodes (acting as a local sink) at the top tier, cluster head (normal node) at the middle tier, and cluster member (normal node) at the lowest tier in the hierarchy. Clustering within normal sensor nodes is done by optimizing the network/coverage lifetime through a cluster-head-selection algorithm and a sleep/wake scheduling algorithm. QoSEC resolves the hot spot problem and prolongs network/coverage lifetime. Secondly, the QoSES addressed the delay-minimization problem in sleep/wake scheduling for event-driven sensor networks for delay-sensitive applications. For this purpose, QoSES assigns different sleep/wake intervals (longer wake interval) to potential overloaded nodes, according to their varied traffic load requirement defined a) by node position in the network, b) by node topological importance, and c) by handling burst traffic in the proximity of the event occurrence node. Using these heuristics, QoSES minimizes the congestion at nodes having heavy traffic loads and ultimately reduces end-to-end delay while maximizing the throughput. Lastly, the QoSEM addresses hot spot problem, delay minimization, and QoS assurance. To address hot-spot problem, mobile sink is used, that move in the network to gather data by virtue of which nodes near to the mobile sink changes with each movement, consequently hot spot problem is minimized. To achieve delay minimization, static sink is used in addition to the mobile sink. Delay sensitive data is forwarded to the static sink, while the delay tolerant data is sent through the mobile sink. For QoS assurance, incoming traffic is divided into different traffic classes and each traffic class is assigned different priority based on their QoS requirement (bandwidth, delay) determine by its message type and content. Furthermore, to minimize delay in mobile sink data gathering, the mobile sink is moved throughout the network based on the priority messages at the nodes. Using these heuristics, QoSEM incur less end-to-end delay, is energy efficient, as well as being able to ensure QoS. Simulations are carried out to evaluate the performance of the proposed protocols of QoSEC, QoSES and QoSEM, by comparing their performance with the established contemporary protocols. Simulation results have demonstrated that when compared with contemporary protocols, each of the proposed protocol significantly prolong the network and coverage lifetime, as well as improve the other QoS routing parameters, such as delay, packet loss ratio, and throughput

Neural Network based Short Term Forecasting Engine To Optimize Energy And Big Data Storage Resources Of Wireless Sensor Networks

Energy efficient wireless networks is the primary research goal for evolving billion device applications like IoT, smart grids and CPS. Monitoring of multiple physical events using sensors and data collection at central gateways is the general architecture followed by most commercial, residential and test bed implementations. Most of the events monitored at regular intervals are largely redundant/minor variations leading to large wastage of data storage resources in Big data servers and communication energy at relay and sensor nodes. In this paper a novel architecture of Neural Network (NN) based day ahead steady state forecasting engine is implemented at the gateway using historical database. Gateway generates an optimal transmit schedules based on NN outputs thereby reducing the redundant sensor data when there is minor variations in the respective predicted sensor estimates. It is observed that NN based load forecasting for power monitoring system predicts load with less than 3% Mean Absolute Percentage Error (MAPE). Gateway forward transmit schedules to all power sensing nodes day ahead to reduce sensor and relay nodes communication energy. Matlab based simulation for evaluating the benefits of proposed model for extending the wireless network life time is developed and confirmed with an emulation scenario of our testbed. Network life time is improved by 43% from the observed results using proposed model

Minimize the end to end delay and Maximize the coverage of Wireless sensor nodes using Data Fusion Distributed Truncated Greedy algorithm

ABSTRACT: As sensors are energy constrained devices, the main challenge is to minimize the end to end delay by maximizing the coverage area of sensor nodes. Instead of considering the whole network for packet transmission, nodes are formed as clusters based on region. To achieve energy balanced clustering in the network we use the concept called Minimum Weight Sub modular Set Cover (MWSSC), Distributed Truncated Greedy Algorithm (DTGA) can provide a better solution for Minimum Weight Sub modular Set Cover (MWSSC). Though it can achieve Minimum Weight Sub modular Set Cover (MWSSC), but it consumes more energy for data transmission and for sharing local information among their neighbour nodes in the same cluster. This problem can be solved by infusing Data Fusion in Distributed truncated greedy algorithm. . These collected data are sent to base station which acts like a control room. Recent advancement in wireless sensor networks has resulted in a unique capability to the remote sensing environment. These systems are often implemented in remote or the areas where it is hard to be reached. Hence, it is difficult in such networks that operate unattended for long durations. Therefore, extending network lifetime through the efficient use of energy has been a key issue in the development of wireless sensor networks Wireless sensor network typically consist of tens to thousands of nodes. These nodes collect process and Cooperatively pass this collected information to a central location The process of grouping the sensor nodes in a densely deployed large-scale sensor network is known as clustering. The intelligent way to combine and compress the data belonging to a single cluster is known as data aggregation in cluster based environment. The information from the nodes to the sink should be in time. If any delay occurs in transferring the packet, then the packet will be useless. So the network should have minimum delay. So optimally choosing the any cast forwarding policy to minimize the expected end-end delay from all sensor nodes to the sink Communication among number of nodes in a large network for relaying packets consumes lot of energy and takes multi-hop to reach sink node. Rather we can form clusters with small numbers of nodes. Initially, clusters will be formed and we can"t assure that the single node has participated in single cluster. This can be solved using Data fusion Distributed truncated greedy algorithm (DTGA). In which there is no need for all the nodes to be in active in the clusters. Only cluster head needs to be in active and the sensor node will wake up when an event occurs. This clearly shows that the network lifetime can be increased through this data fusio

DYNAMIC ROUTING WITH CROSS-LAYER ADAPTATIONS FOR MULTI-HOP WIRELESS NETWORKS

In recent years there has been a proliferation of research on a number of wireless multi-hop networks that include mobile ad-hoc networks, wireless mesh networks, and wireless sensor networks (WSNs). Routing protocols in such networks are of- ten required to meet design objectives that include a combination of factors such as throughput, delay, energy consumption, network lifetime etc. In addition, many mod- ern wireless networks are equipped with multi-channel radios, where channel selection plays an important role in achieving the same design objectives. Consequently, ad- dressing the routing problem together with cross-layer adaptations such as channel selection is an important issue in such networks. In this work, we study the joint routing and channel selection problem that spans two domains of wireless networks. The first is a cost-effective and scalable wireless-optical access networks which is a combination of high-capacity optical access and unethered wireless access. The joint routing and channel selection problem in this case is addressed under an anycasting paradigm. In addition, we address two other problems in the context of wireless- optical access networks. The first is on optimal gateway placement and network planning for serving a given set of users. And the second is the development of an analytical model to evaluate the performance of the IEEE 802.11 DCF in radio-over- fiber wireless LANs. The second domain involves resource constrained WSNs where we focus on route and channel selection for network lifetime maximization. Here, the problem is further exacerbated by distributed power control, that introduces addi- tional design considerations. Both problems involve cross-layer adaptations that must be solved together with routing. Finally, we present an analytical model for lifetime calculation in multi-channel, asynchronous WSNs under optimal power control