EOCC-TARA for Software Defined WBAN

Wireless Body Area Network (WBAN) is a promising cost-effective technology for the privacy confined military applications and healthcare applications like remote health monitoring, telemedicine, and e-health services. The use of a Software-Defined Network (SDN) approach improves the control and management processes of the complex structured WBANs and also provides higher flexibility and dynamic network structure. To seamless routing performance in SDN-based WBAN, the energy-efficiency problems must be tackled effectively. The main contribution of this paper is to develop a novel Energy Optimized Congestion Control based on Temperature Aware Routing Algorithm (EOCC-TARA) using Enhanced Multi-objective Spider Monkey Optimization (EMSMO) for SDN-based WBAN. This algorithm overcomes the vital challenges, namely energy-efficiency, congestion-free communication, and reducing adverse thermal effects in WBAN routing. First, the proposed EOCC-TARA routing algorithm considers the effects of temperature due to the thermal dissipation of sensor nodes and formulates a strategy to adaptively select the forwarding nodes based on temperature and energy. Then the congestion avoidance concept is added with the energy-efficiency, link reliability, and path loss for modeling the cost function based on which the EMSMO provides the optimal routing. Simulations were performed, and the evaluation results showed that the proposed EOCC-TARA routing algorithm has superior performance than the traditional routing approaches in terms of energy consumption, network lifetime, throughput, temperature control, congestion overhead, delay, and successful transmission rate

Stratified Opposition-Based Initialization for Variable-Length Chromosome Shortest Path Problem Evolutionary Algorithms

Initialization is the first and a major step in the implementation of evolutionary algorithms (EAs). Although there are many common general methods to initialize EAs such as the pseudo-random number generator (PRNG), there is no single method that can fit every problem. This study provides a new, flexible, diversity-aware, and easy-to-implement initialization method for a genetic algorithm for the shortest path problem. The proposed algorithm, called stratifiedopposition-based sampling (SOBS), considers phenotype and genotype diversity while striving to achieve the best fitness for the initialization population.SOBS does not depend on a specific type of sampling, because the main goal is to stratify the sampling space. SOBS aims at an initial population with higher fitness and diversity in the phenotype and genotype. To investigate the performance of SOBS, four network models were used to simulate real-world networks. Compared with the most frequently used initialization method, that is, PRNG, SOBS provides more accurate solutions, better running time with less memory usage, and an initial population with higher fitness. Statistical analysis showed that SOBS yields solutions with higher accuracy in 68%–100% of the time. Although this study was focused on the genetic algorithm, it can be applied to other population-based EAs that solve the shortest path problem and use the same direct population representation such as particle swarm optimization (PSO)

FLORA: Fuzzy based Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSNs

Existing Opportunistic routing (OR) schemes work well with asynchronous duty-cycled wireless sensor networks (WSNs), which effectively reduces the sender waiting time by broadcasting packets to a set of forwarders instead of a predetermined forwarder. However, these protocols seriously suffer from the multiple receivers problem which distinctly shortens the network lifetime. Many opportunistic routing protocols view each node as equal importance, neglecting the fact that the nodes close to the sink undertake more duties than the rest of network nodes. Therefore, the nodes located in different positions should play different roles during the routing process. Unlike existing solutions, this paper presents a novel fuzzy logic-driven routing protocol, which consists of three parts. Firstly, each node defines a Routing Zone (RZ) to address the problem resulting from the randomness of node deployment. Secondly, the nodes within RZ are prioritized based on competency value obtained through the fuzzy-logic model. Finally, one of forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio and network lifetime

SDORP: SDN based Opportunistic Routing for Asynchronous Wireless Sensor Networks

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks

FLORA: Fuzzy Based Load-Balanced Opportunistic Routing for Asynchronous Duty-Cycled WSNs

Existing Opportunistic routing (OR) schemes work well with asynchronous duty-cycled wireless sensor networks (WSNs), which effectively reduces the sender waiting time by broadcasting packets to a set of forwarders instead of a predetermined forwarder. However, these protocols seriously suffer from the multiple receivers problem which distinctly shortens the network lifetime. Many opportunistic routing protocols view each node as equal importance, neglecting the fact that the nodes close to the sink undertake more duties than the rest of network nodes. Therefore, the nodes located in different positions should play different roles during the routing process. Unlike existing solutions, this paper presents a novel fuzzy logic-driven routing protocol, which consists of three parts. Firstly, each node defines a Routing Zone (RZ) to address the problem resulting from the randomness of node deployment. Secondly, the nodes within RZ are prioritized based on competency value obtained through the fuzzy-logic model. Finally, one of forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio and network lifetime

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

This paper modeled the multihop data-routing in Vehicular Ad-hoc Networks(VANET) as Multiple Criteria Decision Making (MCDM) in four steps. First, the criteria which have an impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by Fuzzy Membership Functions(FMF) which are interpolated based on the data collected from massive experimental simulations. Third, the Analytical Hierarchy Process(AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and, the TSK inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we designed a distributed and opportunistic data routing protocol, namely, VEFR (Vehicular Environment Fuzzy Router) which targets V2V (vehicle-to-vehicle) communication and runs in two main processes, Road Segment Selection(RSS) and Relay Vehicle Selection(RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results showed that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency

FRCA: A Novel Flexible Routing Computing Approach for Wireless Sensor Networks

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining desired performance due to the complicated and changeable environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the flexibility of routing protocols. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without the interruption of network services. Through extensive performance evaluation studies and simulations, the results demonstrated that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink’s current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink’s position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a Multi-Criteria Decision Making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the Analytic Hierarchy Process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink

Novel Architecture and Heuristic Algorithms for Software-Defined Wireless Sensor Networks

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting-time and the duplicate packets. To this end and beyond the state-of-the-art, this work proposes an SDN-based architecture, namely MINI-SDN, that separates the control and data planes. Moreover, based on MINI-SDN, we propose MINI-FLOW, a communication protocol that orchestrates the computation of flows and data routing between the two planes. MINI-FLOW supports uplink, downlink and intra-link flows. Uplink flows are computed based on a heuristic function that combines four values, the hops to the sink, the Received Signal Strength (RSS), the direction towards the sink, and the remaining energy. As for the downlink flows, two heuristic algorithms are proposed, Optimized Reverse Downlink (ORD) and Location-based Downlink(LD). ORD employs the reverse direction of the uplink while LD instantiates the flows based on a heuristic function that combines three values, the distance to the end node, the remaining energy and RSS value. Intra-link flows employ a combination of uplink/downlink flows. The experimental results show that the proposed architectureand communication protocol perform and scale well with both network size and density, considering the joint problem of routing and load balancing