ANT colony optimization based optimal path selection and data gathering in WSN

A data aggregation is an essential process in the field of wireless sensor network to deal with base station and sink node. In current data gathering mechanism, the nearest nodes to the sink receives data from all the other nodes and shares it to the sink. The data aggregation process is utilized to increase the capability and efficiency of the existing system. In existing technique, the possibility of data loss is high this may leads to energy loss therefore; the efficiency and performance are damaged. In order to overcome these issues, an effective cluster based data gathering technique is developed. Here the optimal cluster heads are selected which is used for transmission with low energy consumption. The optimal path for mobile sink (MS) is done by Ant Colony Optimization (ACO) algorithm. It provides efficient path along with MS to collect the data along with Cluster centroid. The performance of the proposed method is analyzed in terms of delay, throughput, lifetime, etc.</p

DESIGN OF A MINIMAL OVERHEAD CONTROL TRAFFIC TOPOLOGY DISCOVERY AND DATA FORWARDING PROTOCOL FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS

Software-defined networking is a novel concept that is ported into wireless sensor networks to make them more manageable and customizable. unfortunately, the topology discovery and maintenance processes generate high overhead control packet exchange between the sensor nodes and the central controller leading to a deterioration of the network's performance. In this paper, a novel minimal overhead control traffic topology discovery and data forwarding protocol is proposed and detailed. The proposed protocol requires some changes to the topology discovery protocol implemented in SDN-WISE to improve its performance. The proposed protocol has been implemented within the IT-SDN framework for evaluation. The results show reduced overhead control traffic and increase, of about 20%, data packet delivery rate over the protocol in SDN-WISE

Energy and throughput efficient strategies for heterogeneous future communication networks

As a result of the proliferation of wireless-enabled user equipment and data-hungry applications, mobile data traffic has exponentially increased in recent years.This in-crease has not only forced mobile networks to compete on the scarce wireless spectrum but also to intensify their power consumption to serve an ever-increasing number of user devices. The Heterogeneous Network (HetNet) concept, where mixed types of low-power base stations coexist with large macro base stations, has emerged as a potential solution to address power consumption and spectrum scarcity challenges. However, as a consequence of their inflexible, constrained, and hardware-based configurations, HetNets have major limitations in adapting to fluctuating traffic patterns. Moreover, for large mobile networks, the number of low-power base stations (BSs) may increase dramatically leading to sever power consumption. This can easily overwhelm the benefits of the HetNet concept. This thesis exploits the adaptive nature of Software-defined Radio (SDR) technology to design novel and optimal communication strategies. These strategies have been designed to leverage the spectrum-based cell zooming technique, the long-term evolution licensed assisted access (LTE-LAA) concept, and green energy, in order to introduce a novel communication framework that endeavors to minimize overall network on-grid power consumption and to maximize aggregated throughput, which brings significant benefits for both network operators and their customers. The proposed strategies take into consideration user data demands, BS loads, BS power consumption, and available spectrum to model the research questions as optimization problems. In addition, this thesis leverages the opportunistic nature of the cognitive radio (CR) technique and the adaptive nature of the SDR to introduce a CR-based communication strategy. This proposed CR-based strategy alleviates the power consumption of the CR technique and enhances its security measures according to the confidentiality level of the data being sent. Furthermore, the introduced strategy takes into account user-related factors, such as user battery levels and user data types, and network-related factors, such as the number of unutilized bands and vulnerability level, and then models the research question as a constrained optimization problem. Considering the time complexity of the optimum solutions for the above-mentioned strategies, heuristic solutions were proposed and examined against existing solutions. The obtained results show that the proposed strategies can save energy consumption up to 18%, increase user throughput up to 23%, and achieve better spectrum utilization. Therefore, the proposed strategies offer substantial benefits for both network operators and users

A software-defined survivability approach for wireless sensor networks in future internet of the things

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of the Things (IoT) is evolving rapidly, and its significant impacts are expected to affect many application domains. Challenges in areas that humans have been striving to understand, measure, or predict—such as wildlife, healthcare, or environmental hazards—are likely to be addressed by the time IoT emerges. The underlying elements of IoT are wireless sensor networks (WSNs), which consist of a large number of sensor nodes. In the IoT sphere, sensor nodes represent tangible objects—Things—that monitor changes, collect information, and eventually send it through the Internet to a recipient party. Inherently, however, a wireless sensor node relies on limited computational resources with a limited power source. These undesirable qualities result in a low level of dependability. This research explores the viability of applying the unfolding network programmability concepts to overcome survivability obstacles in WSNs and the IoT. In particular, it examines the viability of software-defined networking (SDN) in network lifetime maximisation, failure detection, and failure recovery problems in WSNs. Software-defined networking is a new network programmability concept that separates the traditionally-tied control and data planes. It offloads the route computations and management from network devices to a logically centralised controller. This separation directly leads to better allocation of computational resources for the network nodes and allows endless orchestration possibilities for the controller. This thesis proposes an SDN-based solution to increase the survivability and resilience of WSN environments. Following an approach that conforms with the centralised nature of SDN environments and considers the limited resources of the WSN. A routing algorithm based on A-star was developed for WSNs, then deployed within an SDN environment to maximise the network lifetime. Apart from finding the path with the lowest energy burden, the algorithm offloads most of the control traffic from sensor nodes to the controller. This algorithm resulted in improved resource utilisation among the nodes due to plane decoupling. Additionally, it increased the lifetime of the network by 22.6% compared to the widely explored LEACH protocol. This thesis also investigates different failure detection and recovery practices in the SDN architecture. The simulation results show that adopting bidirectional forwarding detection (BFD) with the asynchronous echo mode for WSN in an SDN environment reduces control traffic for failure detection to between 27% and 48%. The thesis also evaluates the performance of multiple recovery approaches when adopting the premises of SDN. The simulation results indicate that path protection, using group tables from the OpenFlow protocol, has a recovery time up to eight times shorter than the restoration time. The results of the study reveal that using protection as a failure recovery technique significantly reduces control traffic overhead