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Opportunistic Routing (OR) is adapted to improve the performance of low Duty-cycled Wireless Sensor Networks by exploiting its broadcast nature. In contrast to traditional routing, where packets are transmitted along pre-determined paths, OR uses a prioritization metric to select a set of candidates as potential forwarders. This solves the sender's waiting time problem. However, too many candidates may simultaneously wake-up, generating more duplicate packets, occupying the restricted resources and hinder the packet delivery performance. Consciously, to restrict the number of candidates and to counterbalance between the waiting time problem and the duplicate packets problem, this paper proposed a new protocol that combines two main parts. First, each node defines a Candidates Zone (CZ) by a regular geometric shape of four corners. The packets generated by the node will be routed via any path within the CZ. Expressly, the nodes within the CZ are allowed to be selected as candidates. The size of CZ is controlled by the network density. Second, the candidates within the CZ are prioritized based on the OR metric, which is defined as the multiplication of four-distributions: direction distribution, transmission-distance distribution, perpendicular-distance distribution, and residual energy distribution. Through an extensive performance evaluation study and simulation of large-scale scenarios, the results demonstrated that our protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, routing efficiency, sender waiting time, and duplicate packets.

This article modeled the data routing problem in Wireless Sensor Networks as an in-zone random process. The data packets are randomly routed from the source to the sink within the defined Routing Zone via any-path . The proposed “Zone Probabilistic Routing (ZPR)” is a distributed probabilistic and randomized anycast routing protocol. In ZPR, the forwarding probability distribution is defined by multiplying the Four Probability Distributions (4PD) namely: direction, transmission distance, perpendicular distance, and residual energy. In order to meet different performance requirements for different applications, these probability distributions are completely controllable via a set of exponential control-parameters (direction control, transmission distance control, perpendicular distance control, and residual energy control). This set of parameters is user-oriented and can be modified prior to nodes deployment to achieve different performances. Through extensive simulations and experimental results, the optimal values for these exponential control-parameters have been obtained to meet different performance requirements in terms of energy consumption, energy balancing, network lifetime, and delay. Furthermore, through an extensive performance evaluation study and simulation of large-scale scenarios, the results showed that our proposed ZPR protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, and data routing efficiency.

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 stratified opposition-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).

A new approach to implementation of the virtual network embedding strategy over OpenStack cloud platform presented. We presented the OpenStack cloud platform and the problems associated with virtual network embedding. Then, conduct OpenStack dashboard customization to generate virtual network requests to process these requests. We modified OpenStack Compute module to provide bandwidth limitation for the virtual network requests, as well as modification of the data structure used between the OpenStack Horizon and Nova modules, implementing virtual network embedded algorithm (VNE-Greedy) on the top of OpenStack cloud platform.

Although Software-Defined Networking (SDN) is a mature paradigm, failure recovery and management in SDN still need much research attention. OpenFlow, as an implementation of SDN, provides flexible and abstracted approach to configure SDN networks. This Paper focuses on failure recovery mechanism using OpenFlow. The proposed mechanism is divided into: (a) computing the working paths proactively between each source-destination pair and return a list of paths ordered by-path latency from shortest to longest path, (b)implement per-Link Bidirectional Forwarding Detection(BFD) for failure detection, to enable fast detection time, thus fast recovery time, (d) configure OpenFlow Fast Failover Group for restoration such that, the highest-priority bucket to be linked to the shortest path, the second highest-priority bucket linked to the second shortest path and so on. Upon failure, the switch will revert to the second fastest path, (e) to achieve high resource utilization, OpenFlow Select Group used to split the flow among the working paths. The proposed architecture of this work provides high utilization of network resources in addition to efficient fast detection and recovery time. As the evaluation shows, the usage of multipath routing with fast failover scheme provide much resource utilization and fast recovery time.

In this paper, we propose a novel mechanism to calculate sub-regions (overlapped areas) in Wireless Sensor networks (WSNs). As the major part of WSN tasks are monitoring and reporting events in sensors' sensing range, those reported events' locations can be used, by means of convex hulls, to accumulatively learn the boundaries of those overlapped areas between the ranges of sensors. Although the proposed mechanism targeted clustered networks, the mechanism also can be used with non-clustered networks. Besides, the proposed method provides two levels of abstraction, the first level is the selection of the proper algorithm to calculate convex hulls, and the second is the selection of the clustering algorithm at implementation time. The main contribution of this work is to provide a new perspective to solve this problem in WSNs and new avenues for future research.