Socially-aware congestion control in ad-hoc networks: Current status and the way forward

Ad-hoc social networks (ASNETs) represent a special type of traditional ad-hoc network in whicha user’s social properties (such as the social connections and communications metadata as wellas application data) are leveraged for offering enhanced services in a distributed infrastructurelessenvironments. However, the wireless medium, due to limited bandwidth, can easily suffer from theproblem of congestion when social metadata and application data are exchanged among nodes—a problem that is compounded by the fact that some nodes may act selfishly and not share itsresources. While a number of congestion control schemes have been proposed for the traditional ad-hoc networks, there has been limited focus on incorporating social awareness into congestion controlschemes. We revisit the existing traditional ad-hoc congestion control and data distribution protocolsand motivate the need for embedding social awareness into these protocols to improve performance.We report that although some work is available in opportunistic network that uses socially-awaretechniques to control the congestion issue, this area is largely unexplored and warrants more researchattention. In this regards, we highlight the current research progress and identify multiple futuredirections of research

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Algorithms based on spider daddy long legs for finding the optimal route in securing mobile ad hoc networks

Mobile ad hoc networks (MANETs) are wireless networks that are subject to severe attacks, such as the black hole attack. One of the goals in the research is to find a method to prevent black hole attacks without decreasing network throughput or increasing routing overhead. The routing mechanism in define uses route requests (RREQs; for discovering routes) and route replies (RREPs; for receiving paths). However, this mechanism is vulnerable to attacks by malicious black hole nodes. The mechanism is developed to find the shortest secure path and to reduce overhead using the information that is available in the routing tables as an input to propose a more complex nature-inspired algorithm. The new method is called the Daddy Long-Legs Algorithm (PGO-DLLA), which modifies the standard AODV and optimizes the routing process. This method avoids dependency exclusively on the hop counts and destination sequence numbers (DSNs) that are exploited by malicious nodes in the standard AODV protocol. The experiment by performance metrics End-to-End delay and packet delivery ratio are compared in order to determine the best effort traffic. The results showed the PGO-DLLA improvement of the shortest and secure routing from black hole attack in MANET. In addition, the results indicate better performance than the related works algorithm with respect to all metrics excluding throughput which AntNet is best in routing when the pause time be more than 40 seconds. PGODLLA is able to improve the route discovery against the black hole attacks in AODV. Experiments in this thesis have shown that PGO-DLLA is able to reduce the normalized routing load, end-to-end delay, and packet loss and has a good throughput and packet delivery ratio when compared with the standard AODV protocol, BAODV protocol, and the current related protocols that enhance the routing security of the AODV protocols

Can nature help in the routing problem?

To communicate through Internet, we need some intermediate nodes to route information from one extreme to another. These nodes use a routing protocol based on algorithms. Today there are multitude of routing protocols that work quite well in large networks without much changes. Internet is becoming a site with an increasing number of heterogenic changes. We need a protocol to meet these needs. The idea is to develop algorithms based on swarm intelligence. There are several studies about trying to emulate the animal swarms. This algorithms use cooperating individuals through self-organization (with no central control which operates on the swarm members).For this scenario I have ruled out the bees based algorithms that are designed to operate in three dimensions, and I have focused on algorithms based on Ant Colony Optimization (ACO). To reach a conclusion on the effectiveness of these new algorithms, I have relied on a protocol version by J. NetAnts Baras and I have compared it with a traditional protocol such as Ad hoc On-Demand Distance Vector (AODV). To make the implementation and the comparison between the protocols I have used the network simulator NS-2 version 2.34 running under Linux. These two protocols have been tested in very different scenarios to see the limitations of each algorithm and to see which one suits better in each scenario. After the simulations we can conclude that the algorithm NetAnt is not optimal as AODV to operate in Ad-hoc networks. The limitations would be given by the high number of packages that need to be sent to build a path. These packets are sent proactively to all nodes causing congestion and filling the buffers of the nodes. Loops have also been observed to fail to find the correct route. Therefore, the algorithm NetAnt works but is not the most optimal for Ad-hoc network

Capacity Enhancement Approaches for Long Term Evolution networks: Capacity Enhancement-Inspired Self-Organized Networking to Enhance Capacity and Fairness of Traffic in Long Term Evolution Networks by Utilising Dynamic Mobile Base-Stations

The long-term evolution (LTE) network has been proposed to provide better network capacity than the earlier 3G network. Driven by the market, the conventional LTE (3G) network standard could not achieve the expectations of the international mobile telecommunications advanced (IMT-Advanced) standard. To satisfy this gap, the LTE-Advanced was introduced with additional network functionalities to meet up with the IMT-Advanced Standard. In addition, due to the need to minimize operational expenditure (OPEX) and reduce human interventions, the wireless cellular networks are required to be self-aware, self-reconfigurable, self-adaptive and smart. An example of such network involves transceiver base stations (BTSs) within a self-organizing network (SON). Besides these great breakthroughs, the conventional LTE and LTE-Advanced networks have not been designed with the intelligence of scalable capacity output especially in sudden demographic changes, namely during events of football, malls, worship centres or during religious and cultural festivals. Since most of these events cannot be predicted, modern cellular networks must be scalable in terms of capacity and coverage in such unpredictable demographic surge. Thus, the use of dynamic BTSs is proposed to be used in modern and future cellular networks for crowd and demographic change managements. Dynamic BTSs are complements of the capability of SONs to search, determine and deploy less crowded/idle BTSs to densely crowded cells for scalable capacity management. The mobile BTSs will discover areas of dark coverages and fill-up the gap in terms of providing cellular services. The proposed network relieves the LTE network from overloading thus reducing packet loss, delay and improves fair load sharing. In order to trail the best (least) path, a bio-inspired optimization algorithm based on swarm-particle optimization is proposed over the dynamic BTS network. It uses the ant-colony optimization algorithm (ACOA) to find the least path. A comparison between an optimized path and the un-optimized path showed huge gain in terms of delay, fair load sharing and the percentage of packet loss